{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "connected-sperm",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import statsmodels.tsa.stattools as ts\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import statsmodels.api as sm\n",
    "from statsmodels.api import qqplot as qqplot\n",
    "from statsmodels.stats.diagnostic import acorr_ljungbox\n",
    "from statsmodels.graphics.tsaplots import plot_acf,plot_pacf\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "closed-commons",
   "metadata": {},
   "source": [
    "## 加载数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "severe-practitioner",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>销售额</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>日期</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-01-01</th>\n",
       "      <td>242.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-01</th>\n",
       "      <td>235.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-01</th>\n",
       "      <td>232.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-01</th>\n",
       "      <td>178.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-01</th>\n",
       "      <td>184.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-06-01</th>\n",
       "      <td>140.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-07-01</th>\n",
       "      <td>145.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-01</th>\n",
       "      <td>152.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-01</th>\n",
       "      <td>110.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-10-01</th>\n",
       "      <td>130.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01</th>\n",
       "      <td>152.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-01</th>\n",
       "      <td>206.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-01</th>\n",
       "      <td>263.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-02-01</th>\n",
       "      <td>238.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-01</th>\n",
       "      <td>247.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-04-01</th>\n",
       "      <td>193.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01</th>\n",
       "      <td>193.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-01</th>\n",
       "      <td>149.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-07-01</th>\n",
       "      <td>157.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-01</th>\n",
       "      <td>161.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-01</th>\n",
       "      <td>122.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-10-01</th>\n",
       "      <td>130.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-11-01</th>\n",
       "      <td>167.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-01</th>\n",
       "      <td>230.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-01</th>\n",
       "      <td>282.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-01</th>\n",
       "      <td>255.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-01</th>\n",
       "      <td>265.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-01</th>\n",
       "      <td>205.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-01</th>\n",
       "      <td>210.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-01</th>\n",
       "      <td>160.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-01</th>\n",
       "      <td>166.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-01</th>\n",
       "      <td>174.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-09-01</th>\n",
       "      <td>126.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-01</th>\n",
       "      <td>148.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-01</th>\n",
       "      <td>173.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-01</th>\n",
       "      <td>235.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              销售额\n",
       "日期               \n",
       "2018-01-01  242.0\n",
       "2018-02-01  235.0\n",
       "2018-03-01  232.0\n",
       "2018-04-01  178.0\n",
       "2018-05-01  184.0\n",
       "2018-06-01  140.0\n",
       "2018-07-01  145.0\n",
       "2018-08-01  152.0\n",
       "2018-09-01  110.0\n",
       "2018-10-01  130.0\n",
       "2018-11-01  152.0\n",
       "2018-12-01  206.0\n",
       "2019-01-01  263.0\n",
       "2019-02-01  238.0\n",
       "2019-03-01  247.0\n",
       "2019-04-01  193.0\n",
       "2019-05-01  193.0\n",
       "2019-06-01  149.0\n",
       "2019-07-01  157.0\n",
       "2019-08-01  161.0\n",
       "2019-09-01  122.0\n",
       "2019-10-01  130.0\n",
       "2019-11-01  167.0\n",
       "2019-12-01  230.0\n",
       "2020-01-01  282.0\n",
       "2020-02-01  255.0\n",
       "2020-03-01  265.0\n",
       "2020-04-01  205.0\n",
       "2020-05-01  210.0\n",
       "2020-06-01  160.0\n",
       "2020-07-01  166.0\n",
       "2020-08-01  174.0\n",
       "2020-09-01  126.0\n",
       "2020-10-01  148.0\n",
       "2020-11-01  173.0\n",
       "2020-12-01  235.0"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_excel('./作业数据.xlsx',sheet_name='销售额',index_col ='日期' )\n",
    "df['销售额'] = df['销售额'].astype('float')\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "charming-program",
   "metadata": {},
   "source": [
    "## 原始序列的检验"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "italic-basics",
   "metadata": {},
   "source": [
    "时序图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "legislative-bundle",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,5))\n",
    "plt.plot(df['销售额'])\n",
    "plt.title('sales')\n",
    "sns.despine()\n",
    "###解读：非平稳序列，有周期性和趋势性"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "corresponding-literature",
   "metadata": {},
   "source": [
    "自相关图 \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "southern-shell",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_acf(df['销售额'],lags=35).show()\n",
    "###解读：滞后一阶相关性>0.5"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "instructional-valley",
   "metadata": {},
   "source": [
    "平稳性检验"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "deadly-printer",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('原始序列ADF检验结果为；',\n",
       " (-0.6238660726654788,\n",
       "  0.8654975725470296,\n",
       "  10,\n",
       "  25,\n",
       "  {'1%': -3.7238633119999998, '5%': -2.98648896, '10%': -2.6328004},\n",
       "  224.36430489206566))"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"原始序列ADF检验结果为；\",ts.adfuller(df)\n",
    "#解读：p_value=0.8654975725470296>0.05，且adf值 =-0.6238660726654788>-2.6328.接受原假设，即非平稳"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "confident-health",
   "metadata": {},
   "source": [
    "## 一阶差分序列检验"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "living-banana",
   "metadata": {},
   "source": [
    "一阶差分序列时序图、自相关图、平稳性检验"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "focal-handy",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-15.644998068106528,\n",
       " 1.6226751818459924e-28,\n",
       " 10,\n",
       " 24,\n",
       " {'1%': -3.7377092158564813,\n",
       "  '5%': -2.9922162731481485,\n",
       "  '10%': -2.635746736111111},\n",
       " 167.2711090488787)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_d1 = df.diff(1,axis=0).dropna()\n",
    "#时序图\n",
    "plt.figure(figsize=(15,5))\n",
    "plt.plot(df_d1)\n",
    "#解读：在均值附近较为平稳波动\n",
    "\n",
    "#自相关图\n",
    "plot_acf(df_d1,lags=34).show()\n",
    "#解读：有短期相关性，但趋向于0。\n",
    "\n",
    "#平稳性检验\n",
    "ts.adfuller(df_d1)\n",
    "#解读：p_value<0.05,拒绝原假设，即平稳"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "hybrid-antarctica",
   "metadata": {},
   "source": [
    "白噪声检验"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "liquid-maria",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>统计量值</th>\n",
       "      <th>p_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>lag1</th>\n",
       "      <td>0.252254</td>\n",
       "      <td>0.615492</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lag2</th>\n",
       "      <td>2.491964</td>\n",
       "      <td>0.287658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lag3</th>\n",
       "      <td>3.847881</td>\n",
       "      <td>0.278366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lag4</th>\n",
       "      <td>6.222460</td>\n",
       "      <td>0.183139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lag5</th>\n",
       "      <td>6.235142</td>\n",
       "      <td>0.284006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lag6</th>\n",
       "      <td>14.235542</td>\n",
       "      <td>0.027113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lag7</th>\n",
       "      <td>14.455526</td>\n",
       "      <td>0.043648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lag8</th>\n",
       "      <td>19.009419</td>\n",
       "      <td>0.014809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lag9</th>\n",
       "      <td>20.474358</td>\n",
       "      <td>0.015200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lag10</th>\n",
       "      <td>21.554163</td>\n",
       "      <td>0.017544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lag11</th>\n",
       "      <td>22.508152</td>\n",
       "      <td>0.020719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lag12</th>\n",
       "      <td>44.620835</td>\n",
       "      <td>0.000012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lag13</th>\n",
       "      <td>44.809625</td>\n",
       "      <td>0.000023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lag14</th>\n",
       "      <td>45.815201</td>\n",
       "      <td>0.000030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lag15</th>\n",
       "      <td>46.446114</td>\n",
       "      <td>0.000045</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            统计量值   p_value\n",
       "lag1    0.252254  0.615492\n",
       "lag2    2.491964  0.287658\n",
       "lag3    3.847881  0.278366\n",
       "lag4    6.222460  0.183139\n",
       "lag5    6.235142  0.284006\n",
       "lag6   14.235542  0.027113\n",
       "lag7   14.455526  0.043648\n",
       "lag8   19.009419  0.014809\n",
       "lag9   20.474358  0.015200\n",
       "lag10  21.554163  0.017544\n",
       "lag11  22.508152  0.020719\n",
       "lag12  44.620835  0.000012\n",
       "lag13  44.809625  0.000023\n",
       "lag14  45.815201  0.000030\n",
       "lag15  46.446114  0.000045"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "re = acorr_ljungbox(df_d1)#返回统计量，p值\n",
    "#解读：p值>0.05，不能拒绝原假设，一阶差分序列是白噪声\n",
    "re  = pd.DataFrame(re).T\n",
    "lag_num = ['lag'+str(i+1) for i in range(len(re))]\n",
    "re.columns=['统计量值','p_value']\n",
    "re.index = lag_num\n",
    "re"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "declared-poison",
   "metadata": {},
   "source": [
    "## 定阶"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "superb-control",
   "metadata": {},
   "source": [
    "人工定阶"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "id": "stock-fight",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x840 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x840 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#自相关图\n",
    "plot_acf(df_d1,lags=34).show()\n",
    "#解读：1阶以后落在置信区间内，3阶截尾，\n",
    "\n",
    "#偏自相关图\n",
    "plot_pacf(df_d1,lags=20).show()\n",
    "#解读：偏自相关系数震荡，视为拖尾\n",
    "\n",
    "#因此，ARIMA模型为ARIMA(0,1,3)，或许MA（3）模型拟合会比较好？"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "preliminary-disorder",
   "metadata": {},
   "source": [
    "统计学方法，通过AIC和BIC确定--越小越好"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "abstract-premiere",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pmax = int(len(df_d1)/10) #一般阶数不超过样本量/10\n",
    "qmax = int(len(df_d1)/10) #一般阶数不超过样本量/10\n",
    "pmax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "characteristic-spanish",
   "metadata": {},
   "outputs": [],
   "source": [
    "#不同p和q组合下的bic取值\n",
    "bic_matrix=[]\n",
    "for p in range(pmax+1):\n",
    "    tmp = []\n",
    "    for q in range(qmax+1):\n",
    "        try:\n",
    "            tmp.append(sm.tsa.ARIMA(df,(p,1,q)).fit().bic)\n",
    "        except:\n",
    "            tmp.append(None)\n",
    "    bic_matrix.append(tmp)          "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "victorian-architect",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('BIC规则下最佳p、q组合', (0, 3))"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bic_matrix = pd.DataFrame(bic_matrix)\n",
    "p,q = bic_matrix.stack().idxmin()\n",
    "\"BIC规则下最佳p、q组合\",(p,q)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "pregnant-commonwealth",
   "metadata": {},
   "outputs": [],
   "source": [
    "#不同p和q组合下的aic取值\n",
    "aic_matrix=[]\n",
    "for p in range(pmax+1):\n",
    "    tmp = []\n",
    "    for q in range(qmax+1):\n",
    "        try:\n",
    "            tmp.append(sm.tsa.ARIMA(df,(p,1,q)).fit().aic)\n",
    "        except:\n",
    "            tmp.append(None)\n",
    "    aic_matrix.append(tmp)      "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "embedded-yukon",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('AIC规则下最佳p、q组合', (3, 1))"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aic_matrix = pd.DataFrame(aic_matrix)\n",
    "p,q = aic_matrix.stack().idxmin()\n",
    "\"AIC规则下最佳p、q组合\",(p,q)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "combined-packet",
   "metadata": {},
   "source": [
    "## 建模及预测"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "sapphire-scene",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>ARIMA Model Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>       <td>D.销售额</td>      <th>  No. Observations:  </th>    <td>35</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>          <td>ARIMA(3, 1, 1)</td>  <th>  Log Likelihood     </th> <td>-167.027</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>css-mle</td>     <th>  S.D. of innovations</th>  <td>26.960</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>          <td>Fri, 26 Feb 2021</td> <th>  AIC                </th>  <td>346.055</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>              <td>09:48:50</td>     <th>  BIC                </th>  <td>355.387</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Sample:</th>           <td>02-01-2018</td>    <th>  HQIC               </th>  <td>349.276</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                 <td>- 12-01-2020</td>   <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "       <td></td>          <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>       <td>    0.6067</td> <td>    0.904</td> <td>    0.671</td> <td> 0.502</td> <td>   -1.164</td> <td>    2.378</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L1.D.销售额</th> <td>    0.7954</td> <td>    0.142</td> <td>    5.590</td> <td> 0.000</td> <td>    0.517</td> <td>    1.074</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L2.D.销售额</th> <td>    0.2144</td> <td>    0.190</td> <td>    1.129</td> <td> 0.259</td> <td>   -0.158</td> <td>    0.587</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L3.D.销售额</th> <td>   -0.5244</td> <td>    0.145</td> <td>   -3.624</td> <td> 0.000</td> <td>   -0.808</td> <td>   -0.241</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L1.D.销售额</th> <td>   -1.0000</td> <td>    0.079</td> <td>  -12.645</td> <td> 0.000</td> <td>   -1.155</td> <td>   -0.845</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<caption>Roots</caption>\n",
       "<tr>\n",
       "    <td></td>   <th>            Real</th>  <th>         Imaginary</th> <th>         Modulus</th>  <th>        Frequency</th>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>AR.1</th> <td>          -1.4823</td> <td>          -0.0000j</td> <td>           1.4823</td> <td>          -0.5000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>AR.2</th> <td>           0.9455</td> <td>          -0.6264j</td> <td>           1.1342</td> <td>          -0.0931</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>AR.3</th> <td>           0.9455</td> <td>          +0.6264j</td> <td>           1.1342</td> <td>           0.0931</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>MA.1</th> <td>           1.0000</td> <td>          +0.0000j</td> <td>           1.0000</td> <td>           0.0000</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                             ARIMA Model Results                              \n",
       "==============================================================================\n",
       "Dep. Variable:                  D.销售额   No. Observations:                   35\n",
       "Model:                 ARIMA(3, 1, 1)   Log Likelihood                -167.027\n",
       "Method:                       css-mle   S.D. of innovations             26.960\n",
       "Date:                Fri, 26 Feb 2021   AIC                            346.055\n",
       "Time:                        09:48:50   BIC                            355.387\n",
       "Sample:                    02-01-2018   HQIC                           349.276\n",
       "                         - 12-01-2020                                         \n",
       "===============================================================================\n",
       "                  coef    std err          z      P>|z|      [0.025      0.975]\n",
       "-------------------------------------------------------------------------------\n",
       "const           0.6067      0.904      0.671      0.502      -1.164       2.378\n",
       "ar.L1.D.销售额     0.7954      0.142      5.590      0.000       0.517       1.074\n",
       "ar.L2.D.销售额     0.2144      0.190      1.129      0.259      -0.158       0.587\n",
       "ar.L3.D.销售额    -0.5244      0.145     -3.624      0.000      -0.808      -0.241\n",
       "ma.L1.D.销售额    -1.0000      0.079    -12.645      0.000      -1.155      -0.845\n",
       "                                    Roots                                    \n",
       "=============================================================================\n",
       "                  Real          Imaginary           Modulus         Frequency\n",
       "-----------------------------------------------------------------------------\n",
       "AR.1           -1.4823           -0.0000j            1.4823           -0.5000\n",
       "AR.2            0.9455           -0.6264j            1.1342           -0.0931\n",
       "AR.3            0.9455           +0.6264j            1.1342            0.0931\n",
       "MA.1            1.0000           +0.0000j            1.0000            0.0000\n",
       "-----------------------------------------------------------------------------\n",
       "\"\"\""
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#（1）建模\n",
    "model = sm.tsa.ARIMA(df,(3,1,1)).fit()\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "serial-homeless",
   "metadata": {},
   "source": [
    "残差检验"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "institutional-piece",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "resid = model.resid\n",
    "#自相关图\n",
    "plot_acf(resid,lags=34).show()#----从1阶开始都很小，说明相关性都很小\n",
    "\n",
    "#偏自相关图\n",
    "plot_pacf(resid,lags=14).show()#----从1阶开始都很小，说明相关性都很小\n",
    "\n",
    "#说明很有可能是纯随机序列"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "alien-equation",
   "metadata": {},
   "source": [
    "图检验：QQ图--判断残差是否符合正态分布--线性即正态分布"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "chicken-gregory",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "qqplot(resid,line='q',fit=True).show()\n",
    "#解读：残差服从正态分布，均值为0，方差为常数"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "arabic-mining",
   "metadata": {},
   "source": [
    "统计学检验：DW检验--检验自相关性最常用的方法，但只适用于检验一阶自相关，因为自相关系数p的值结余-1和1之间，所以0<=DW<=4,\n",
    "DW=0  == p=1,即存在正自相关；DW=4  == p=-1,即存在负自相关；DW=2  == p=0,即不存在（一阶）自相关性；"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "thick-destruction",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.1530268865683935"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sm.stats.durbin_watson(resid.values)\n",
    "#解读：不存在一阶自相关"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "imperial-freeze",
   "metadata": {},
   "source": [
    "Ljung-Box检验（LB检验）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "acute-bloom",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0.46284027]), array([0.49629955]))"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "acorr_ljungbox(resid,lags=1)\n",
    "#p_value>0.05,resid是白噪声\n",
    "#如果残差不是白噪声，可考虑另外一组p、q值，或者该数据不适应ARIMA模型"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "broke-doubt",
   "metadata": {},
   "source": [
    "（3）预测"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ruled-ecuador",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "id": "quick-joseph",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>248.728814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>260.141366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>239.959725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>219.465420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>193.164380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>178.746463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>172.699993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>178.905054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>190.418073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>204.389291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>215.028594</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>220.760868</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             0\n",
       "0   248.728814\n",
       "1   260.141366\n",
       "2   239.959725\n",
       "3   219.465420\n",
       "4   193.164380\n",
       "5   178.746463\n",
       "6   172.699993\n",
       "7   178.905054\n",
       "8   190.418073\n",
       "9   204.389291\n",
       "10  215.028594\n",
       "11  220.760868"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "values,std,rag = model.forecast(12) #预测值，标abs准差，置信区间\n",
    "pd.DataFrame(values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "defined-opposition",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#进行绘制预测图像\n",
    "ax=plt.plot(df.ix['2018-01-01':],label=\"observed\")\n",
    "# values.predicted_mean.plot(ax=ax,label=\"static ForCast\",alpha=.7,color='red',linewidth=5)\n",
    "#在某个范围内进行填充\n",
    "plt.fill_between(rag[:,0],rag[:,1], color='k', alpha=.2)\n",
    "# ax.set_xlabel('Date')\n",
    "# ax.set_ylabel('CO2 Levels')\n",
    "plt.legend()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "furnished-banner",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2018-01-01'"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index = df.index.astype(str)\n",
    "df.index[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "centered-journalism",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>销售额</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>日期</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-01-01</th>\n",
       "      <td>242.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-01</th>\n",
       "      <td>235.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-01</th>\n",
       "      <td>232.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-01</th>\n",
       "      <td>178.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-01</th>\n",
       "      <td>184.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-06-01</th>\n",
       "      <td>140.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-07-01</th>\n",
       "      <td>145.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-01</th>\n",
       "      <td>152.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-01</th>\n",
       "      <td>110.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-10-01</th>\n",
       "      <td>130.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01</th>\n",
       "      <td>152.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-01</th>\n",
       "      <td>206.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-01</th>\n",
       "      <td>263.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              销售额\n",
       "日期               \n",
       "2018-01-01  242.0\n",
       "2018-02-01  235.0\n",
       "2018-03-01  232.0\n",
       "2018-04-01  178.0\n",
       "2018-05-01  184.0\n",
       "2018-06-01  140.0\n",
       "2018-07-01  145.0\n",
       "2018-08-01  152.0\n",
       "2018-09-01  110.0\n",
       "2018-10-01  130.0\n",
       "2018-11-01  152.0\n",
       "2018-12-01  206.0\n",
       "2019-01-01  263.0"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.ix['2018-01-01':'2019-01-01']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "id": "vanilla-afghanistan",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#实际值、预测值、差分预测值作图\n",
    "fig1,ax=plt.subplots(figsize=(18,5))\n",
    "ax=plt.plot(df.ix['2018-01-01':]) #实际值\n",
    "ax1=plt.plot(values,'ro') #预测值\n",
    "\n",
    "\n",
    "# fig2=model.predict('2021-01-01','2021-05-01',dynamic=False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "demonstrated-single",
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'2018-01-01'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32md:\\python3.7.3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   2656\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2657\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2658\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: '2018-01-01'",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32md:\\python3.7.3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py\u001b[0m in \u001b[0;36m_get_index_label_loc\u001b[1;34m(self, key, base_index)\u001b[0m\n\u001b[0;32m    432\u001b[0m                 \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mint\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minteger\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 433\u001b[1;33m                     \u001b[0mloc\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrow_labels\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    434\u001b[0m                 \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32md:\\python3.7.3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   2658\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2659\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_maybe_cast_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2660\u001b[0m         \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtolerance\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtolerance\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: '2018-01-01'",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-90-28e91f54c6db>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mpredict_sunspots\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstart\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'2018-01-01'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mend\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'2020-05-01'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mdynamic\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32md:\\python3.7.3\\lib\\site-packages\\statsmodels\\base\\wrapper.py\u001b[0m in \u001b[0;36mwrapper\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m    113\u001b[0m             \u001b[0mobj\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwrap_output\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mresults\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mhow\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mhow\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    114\u001b[0m         \u001b[1;32melif\u001b[0m \u001b[0mhow\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 115\u001b[1;33m             \u001b[0mobj\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwrap_output\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mresults\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mhow\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    116\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mobj\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    117\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32md:\\python3.7.3\\lib\\site-packages\\statsmodels\\tsa\\arima_model.py\u001b[0m in \u001b[0;36mpredict\u001b[1;34m(self, start, end, exog, typ, dynamic)\u001b[0m\n\u001b[0;32m   1857\u001b[0m     def predict(self, start=None, end=None, exog=None, typ='linear',\n\u001b[0;32m   1858\u001b[0m                 dynamic=False):\n\u001b[1;32m-> 1859\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mparams\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstart\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mend\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mexog\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtyp\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdynamic\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1860\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1861\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_forecast_error\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msteps\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32md:\\python3.7.3\\lib\\site-packages\\statsmodels\\tsa\\arima_model.py\u001b[0m in \u001b[0;36mpredict\u001b[1;34m(self, params, start, end, exog, typ, dynamic)\u001b[0m\n\u001b[0;32m   1216\u001b[0m         \u001b[1;31m# go ahead and convert to an index for easier checking\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1217\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstart\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mstr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdatetime\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1218\u001b[1;33m             \u001b[0mstart\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0m_\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0m_\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_index_label_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstart\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1219\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstart\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mslice\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1220\u001b[0m                 \u001b[0mstart\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mstart\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstart\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32md:\\python3.7.3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py\u001b[0m in \u001b[0;36m_get_index_label_loc\u001b[1;34m(self, key, base_index)\u001b[0m\n\u001b[0;32m    463\u001b[0m                 \u001b[0mindex_was_expanded\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    464\u001b[0m             \u001b[1;32mexcept\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 465\u001b[1;33m                 \u001b[1;32mraise\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    466\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mloc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mindex_was_expanded\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    467\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32md:\\python3.7.3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py\u001b[0m in \u001b[0;36m_get_index_label_loc\u001b[1;34m(self, key, base_index)\u001b[0m\n\u001b[0;32m    427\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    428\u001b[0m             loc, index, index_was_expanded = (\n\u001b[1;32m--> 429\u001b[1;33m                 self._get_index_loc(key, base_index))\n\u001b[0m\u001b[0;32m    430\u001b[0m         \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    431\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32md:\\python3.7.3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py\u001b[0m in \u001b[0;36m_get_index_loc\u001b[1;34m(self, key, base_index)\u001b[0m\n\u001b[0;32m    361\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mdate_index\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    362\u001b[0m             \u001b[1;31m# (note that get_loc will throw a KeyError if key is invalid)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 363\u001b[1;33m             \u001b[0mloc\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mindex\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    364\u001b[0m         \u001b[1;32melif\u001b[0m \u001b[0mint_index\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mrange_index\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    365\u001b[0m             \u001b[1;31m# For Int64Index and RangeIndex, key is assumed to be the location\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32md:\\python3.7.3\\lib\\site-packages\\pandas\\core\\indexes\\datetimes.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   1022\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mIndex\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstamp\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtolerance\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1023\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1024\u001b[1;33m                 \u001b[1;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1025\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mValueError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1026\u001b[0m                 \u001b[1;31m# list-like tolerance size must match target index size\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: '2018-01-01'"
     ]
    }
   ],
   "source": [
    "predict_sunspots = model.predict(start='2018-01-01',end='2020-05-01',dynamic=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "monthly-symbol",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No handles with labels found to put in legend.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x500 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot\n",
    "plt.figure(figsize=(12,5), dpi=100)\n",
    "\n",
    "# plt.plot(df['销售额'], label='actual')\n",
    "\n",
    "plt.fill_between( pd.DataFrame(rag)[0],pd.DataFrame(rag)[1], \n",
    "                 color='k', alpha=.15)\n",
    "plt.title('Forecast vs Actuals')\n",
    "plt.legend(loc='upper left', fontsize=8)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "id": "joint-wichita",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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SNa7Wnu/r1mL9cOujryvCmsVe+O3oA5y5kcgcT0wrxKofL+OTRQPg1LnhleIFxRWqfup3HmYy713PQk9HiL4ulvDqYQVPF0uIdav+nng8HnOx4m5MFsIeZsKzu0Wjn4cQQtoDhUKJrUcfQFlrQY6+jhBzx7lyk1QtLl1MsOnfXVPJGcXM8RNXEpCRU4qVs/s80yrx/KIK3I7OwM3IDIQ9fLbZHZo42Bign5sV+rlZoltnY1WB/2FSHrPz7OilWIwd2AXGEm6HsxLSGIWFTz+DVlRoXuiwdOlSpKSkAKhaiNfSrQsJIc2LCuyE/Ku/mxW+emcI1m4PQW5hOXPcPyAST7JL8ObUXhDW6pVbUibF7eh/B8dFZTDbRp+FtpZANYSvr6slDMXazH3MjHQxyceRGeB38XYyJvs4tugAI0IIaS0Kiiuw5yzbD9fZzggj+nbmICPNBAI+3nqlF2zM9bHjZARTpMktrMCHP1/Fqtl9MKAH28/xSVaxakdU1KMcZtfVszA31oWXuxUGuFvDraspREK29/vYgfY4GZTADNz+IyASL3Uz56QFAiGEcO1SWAqik/KY+KwxLjCSsJ/buWJlqo+vl3vjqz9CcS82mzkeEpGOD3+6ijWLvWBqqL5rSqlU4tGTQlXrl5jkPOa96lloCfnwcDZHPzcr9HWxrHN31jxfNwSHp0Emf/okZRVy7P/7Id56xaPxT0wIR+zs7FS3d+3ahTFjxkBLS31Omq+vb0unRQhpQe2uwB4fH9+oFy5/f/96t3Vcu3YN+/fvx507d5Cfnw8jIyMMGjQIb7zxBpycnJoiZdKKONoa4dv3vLF2ewgSUguY44HBScjIKcXq+f1QWi6tGhwXno4H8dnP1XfQWKKN/u5W8HK3gkc3c2iJBA0+5pXh3XD2RhKKStnt+58tGdjoHAghpK3bczYaJWXsoOilU3q1umIwj8fDZB8nWJro45t9t1FRa+V5RaUc63eGYtEEd0wY6ojYx3kIDk9DaGS6xtWIz8LJ1hBePazh5W4Fe2uDBldMiYR8zPV1xde7b6nFE54U4PKdFAzv03ouWhBCSEsoLZdiZ0AEE+9sKYFvE/Y1bypiXRE+XTIQvxy+j79D2JY2CakFWPnDFaxZPAA25vq4H5uN0Mh03IrKQE4Bu9DoWZgZ6aKfqyX6uVmip5MZdLQaLjVYm+lj7EB7BFxVn4cVGJyEid6Oql28hLR2I0eORKdOnZCamor79+/jlVdewfvvvw9vb+9nPodSqcQ///yDQ4cO4cGDBygoKIBYLEaPHj0wY8YMjBo1qs7HVlRUYN++fTh79izi4uJQXl4OQ0NDuLm5PdNj9+7di8DAQMTHx6O0tBQSiQSurq6YPn16vfU1pVKJs2fP4tChQwgPD0dpaSksLCwwePBgLFq0CPb29sxjNm/ejC1btqBTp064cOECiouL4e/vj9OnT+PJkycQCoXw8PDAsmXL4OmpeZd+YWEh/vjjD1y4cAGJiYmQSqUwNjaGh4cH5s+fj379+tX9F91ERowYgdTUVCxbtgzLly/XeJ+aQ01rmzdvHhN7+PCh2s9jxoxBYmIihgwZgu3btzf4HP/973+xYMGCRvwWpCm1uwL7gwds/9XnoVAo8Mknn+DQoUNq8aysLBw/fhynT5/G2rVrMXXq1CZ5PtJ6mBrq4qt3hmDTntsIjUxnjt+NzcLiz/9G6XOsUgeALlYS9He3woAe1nCyNWp08UdfV4QZo53x+/FwtXhYdCbuxWTBw9n8ufIihJC2KD4lH4HBiUx8VD+7JhtQ3RwG9rTGV28PwbodwUzPdKUS2H4iAvv/fvhcO6KEAj56dTODl7sV+rtZwcyo8X3dh3jY4OglI8TWmh2y+0wUBveyeaYLwoQQ0l4cPB+DvCK27cPSyT2Zna2thVDAx7JpHuhkrg//gEjmeE5BOd7fHAQolWrtJ58Vjwd0tzNWtX55lgu4mswY3R3/3ExWaz8jVyix63Qk/ju/f6PPR+qWX1SB7/eH4X5cNjMMtz0TCfno5WSG92Z4NttuE21tbfz00094/fXXkZ2djZiYGCxZsgTdunXDrFmzMGXKFOjq1v15rKSkBCtXrsTFixfV4nl5eQgKCkJQUBCmTp2K9evXM//OsrOzsXDhQsTExKjFc3JyVI99++238e677zLPW1RUhLlz5yIqKkotnp+fjxs3buDGjRsIDg7G2rVrmccWFxdj5cqVuHTpklo8JSUFBw4cwJEjR7BmzRpMnz69zt87IyMDixcvRmys+g79a9euITQ0FLt370bv3r3VjiUkJGDhwoVIT1ev1WRmZuLcuXM4f/481q1bh2nTptX5vG3FpEmT8MMPPyA4OBi5ubkwMTFh7nPmzBkAgFAoxIQJE1o6RVJDuyuw37t3T3V72rRpzLac2qysrDTG16xZo1Zcd3Nzg6urK1JSUhAaGgqpVIr/+7//g4GBQb1XA0nbpKstxP8t7I8dJ8JxIiiBOd6Y4jqfz4O7gym8elQVOqzN9F84v3GD7HEiKAEZtfq7+5+KwLdOPq1uxSYhhDQHpVKJ3zT0w9XTEWLe+NbRD7c+Tp2NsGmFD9ZuD9bYP74xxXWxrgh93SwxwN0avbubv3DfeR6Ph4UT3PF/P6sP4srKK8Opa48wZRjt4iOEdAypWcU4fiWeiQ/qZd3qF7bweDxMHd4Nlqb6+HbvbaaQXilt3PwOPR0hPLtboJ+bFfq4WGhsadlYhmJtvDLCCXvOqLd6u34/DdFJuXDpwhaUyPP5fn8Ybkezw9bbO6lMgdvRmfh+fxg+bcYd366urjh+/Dg2bNiAgIAAKBQKxMbG4rPPPsNPP/2E9957r86i7wcffICLFy/C0NAQb775JgYMGACJRIK0tDQcPnwYx44dw5EjR+Dh4YEZM2aoPfazzz5TFdcXLVqE8ePHw8jICNnZ2dixYwcCAwPx66+/YtKkScyK8p9//llVXJ8+fTpeffVVmJiYIDs7G3v37sXJkydx4MABTJw4EX379lU9TqlU4t1338XVq1cBAH5+fpg1axbMzc1x//59bN68GYmJifjkk08gFos1roKvrKzEsmXLEB8fj7lz56oWrx44cAD79++HVCrFjz/+CH9/f7XHrVq1Cunp6RAKhVixYgV8fHygr6+PtLQ0bN68GaGhoVi/fj3Gjh0LiUTSiP+CTc/a2hqXL19W/Zyeno7XXnsNAPD9998zFw9qmzhxIn788UfIZDKcOXMGs2fPZu5TXWAfOnQoTE1NmzB70ljttsBuZGSEzz///LnOcfjwYfz1118Aqq4CffHFF5g8ebLq+OXLl/HOO++oiux9+/aFkVHDA8lI2yLg87Bkck/YmOlj67EHjep1q6stgKeLpaqfukSv/gs9jSUSCjDP1xUb99xWi8enFODK3VQM87Rt0ucjhJDW6PKdVEQl5jLxmS93bzPD0cyNdbFh2RB8vftWo7/0Wpnqwcu9qvWLm4MJBE28irKnoxn6uVniZmSGWvzg+RiM7m8HcRO/txFCSGu07Xi4Wo9woKrH+KIJPTjKqPEG97KBuZEu1u0IQb6Glfj16WQuRj83S/R3s4Krg0mzrNif5O2I09cSmTlY/icj8NU7Q2gYZBPRNEOgI2mJ39/MzAwbN27EO++8gwMHDuDgwYMoLi5GdnY2Pv74YwQFBWHjxo3Q1n56cer69es4f/48RCIRdu/eje7du6uOde7cGf3791etRt+7d69agb24uBhBQUHQ0tKCn58fVq9erTpma2uLjRs3IiQkBPn5+bh27RpTYI+Pr7p46OTkhHXr1qk9r4eHBx49eoSKigo8fvxYrcB++vRpVXH9nXfewYoVK1TH7Ozs4OPjgxkzZiAuLg6ff/45hg0bBj09PbXnzsrKQnZ2Nr755huMHz9eFf/ss8/w+PFjXL9+HXfu3FF7TFxcHGJjY6GlpYWlS5di6dKlajl/9913GDJkCEpLSxEWFgYfH596/ms1P4FAoLaoVyZ7unjGxMSkzgW/1WxtbdGnTx/cunULAQEBTIE9OTkZERFV7csmTZrUhJmT59GuCuwVFRWqK3dubm7PdY7S0lJ8//33qp8//PBDteI6APj4+GDp0qXYsmULCgoKsG3bNqxater5Eyet2vghXWFpqo+vd99EWUXdqzzMDHX+7adujZ5OphAJm3f7/BCPTjh6KQ5xKeq94qu271s3+/MTQgiXyipk8D/J9sO1tRBj/OCuHGT0/PR0RPhkkRe2HQ9HwLVH9d63u51x1XtNDyvYWUqavegwf7wbbkdlqF1kLi6T4q9/YrFwgnuzPjchhHDt5r99yWt7ZUQ3WJroaXhE6+VsZ4xvVnhj7fZgJKUX1Xk/oYCHHl2rLrD2dbOEjVnz90HX0RJi1hgXbPnrrlo88lEuQiPS4aVh8DdpPJcuxh1yBXs1ly4t1zrQ3t4eq1evxltvvYVt27Zh27ZtkMvlCAwMhFAoxLfffqu6b0BAAABAKpVi4sSJ9Z43NjYWxcXFEIur/l2KxWLcvXu3zvvXbBuTl8deYHB0dMTly5eRnZ2NvLw8GBs//Tvi8/k4fPiwxvMePXoUAGBubo633nqLOS6RSPCf//wHb7/9NnJycnD58mWMGzeOud/06dPViuvVhg4diuvXr6OsrEwt7uTkVGdbaKVSiYcPH0IgEEAmk2n8fduiSZMm4datW7hz5w6ePHkCGxsb1bHTp08DAAwMDOrs9U5aTrsqsEdEREAqrRpy1qtXr+c6x/Hjx5GZWfWm061bN41bMABgzpw52LZtG8rLy3HkyBG89957EArb1V8nqaGvqyU2LBuKdTtCkJX39EW+q42hqtDh2MmwRVdX8PlV2/c/+uW6WjwztxSnriViso9ji+VCCCEt7a9/YpiVbgCwZHJPiIStsx9ufQQCPpZO7QVrc31sPxEBxb8VbZGQj5eczeHlboV+blYwMWjZlfldrAwwsp8dzoU+VoufvJqA8UMcYGHctgpMhBDyrKQyOTPzCKjaeTR1eNtsk2VhoocNy4Zi4x71XVNGYm30ca1q/dLb+cXbjD2PUf064/iVeCRnqBf/d56KRF9XyybfpdURvTfDs8P3YG9pBgYG+M9//oOhQ4fizTffRHFxMU6dOoWZM2eqBnHWHmxZH6VSiezsbFWBvaa0tDSEhYUhJiYGcXFxuH//vqq2Vf3Y2hYuXIgjR44gPz8fEyZMwMiRI+Hi4oJu3brBzc2NWXVerXrVdJ8+fSASaX69GDjwaTue8PBwjQX2+fPna3zss3SISExMxN27dxEbG4vY2Fjcu3cP+flPZwcpFO3j/+Njx47FunXrUFlZiVOnTmHJkiWqY9XtYcaNG9dge2zS/NpVRbhm//W6pg035Pz586rb06ZNA5+v+Y3c2NgYo0ePxsmTJ5GTk4PIyMjnLuqTtsHBxhC/rB6JGw/SAKUSbg6msOB45UovJ3P0cbFgViIcPP8Qo/rbQazb8h+OCSGkuT3JLsbRS2w/XC93K3h2t+Ago6YzcagjvNytcTcmE8YSHfRyMoOONrcf12aPdcHlO6lqvXqlMgX2no3G/5vZ8l9WCSGkJRy7HI+07BImvnhCD+hotd2v0fq6Ivzv9QG4G5OF7PwydLE2gJOtEecznAQCPhaMd8O6HSFq8ZTMYpy/+RhjBthzk1g7YiTRbtYe5B1ZdYthc3NzDBs2jDner18/rFq1Cp9++ikA4NKlS6oCe1FRkeo+mzZtavC5avfZjo6OxhdffIHQ0FBVjM/no0uXLpgxYwauXLmCJ0+eaDyXhYUFDh06hK1bt+LChQvYv3+/6phIJMLgwYPxzjvvMLWuwsKq2UGGhoZ15qmnpweRSASpVKq6f01isRiOjo1fFBgSEoINGzaoivxAVWtnR0dHTJ48GQcPHkRpaWk9Z2hbDAwMMHz4cAQGBiIgIEBVYE9MTFT1z58yZQqXKZJ/td1PBhpUb40RCATo06dPox+vVCrVttd4e3vXe/9evXrh5MmTAID79+9Tgb0D0BYJWl1/8wV+7gh7mKk25K+oVIpD/8RggR9t3yeEtD/bj0dAJldflSIS8vH6pLbTD7c+liZ6raqQYGqoi0neXfHXP7Fq8Yu3kzHZxxEONnV/uSKEkLYop6AMB8/HMPFeTmYY1Kvttyvh8Xjo3QovSPdzs4R7V1NEJOSoxfeejYZPb1vOLzgTUpf//e9/kMvl8PT01FhgB4D+/furblcX1QGoBnEWFxc32JO7tkePHmHWrFkoKSmBq6sr5syZgx49esDBwUHV533q1Kl1FtiBqt7l69atw7p165CVlaVa/X7s2DFcunQJV69exY4dO+Dl5aWWc15eHgoKCuo8b2lpqarDhKZhowYGBo36XQHg5s2bWLx4MaRSKfr374/XXnsNrq6usLOzU62kP3bsWLsqsANVbWICAwMRHR2N+Ph4ODo6qtrD2NvbNzgslbSMdrXPqnoFe5cuXXD8+HG8/vrrGDFiBHr27InBgwdj5syZ+P3335GVlaXx8RkZGSguLgZQtSXFwcGh3udzdXVV3a7vBatabm7uC/9pL32kSNOxtzbAiL6dmfjJoAS1djaEENIe3IrKQGhkOhOfMswJVqb6HGTUMbwyvBszsFupBHYGRHKUESGENJ+dAZEor1SfvcTn87Bkck8auNmMeDweFvqxs9Tyiipw/Aq7c42Q1sLCouqCVVJSUp2tSVJSUlS3LS0tVberh5rGxcWptTip6dKlS5g7dy7mzp2r1lLmjz/+QElJCXR1dbFz5068+uqrcHFxURXX5XK5qsZVW2VlJUJCQhASEqIqlJubm2PgwIFYunQpjh07Bnt7e8hkMvzyyy9qj3V3r1rId/v2bVURvbYbN26obvfowS6CeZ7X0q1bt0IqlcLGxgbbt2+Hn58fHB0dVcX10tJSVFZWNvq8LeV53z+8vb1V/fGre/ZXt4eh4aatR7spsGdkZCAtLQ0AkJCQgLVr1yIoKAipqamorKxEdnY2wsLCsGnTJowZMwb79u1jzpGamqq63VBxHaia+lvz+RsycODAF/7j6+v7LH8dpIOZPcYVWrV6DlfKFNgbGMVRRoQQ0vSkMgW2HWcHG5kZ6mDaiG4cZNRx6OuKMONlZyYe9jATd2M67sA0Qkj7E/koB5fCUpi47yB72Fs3fsUlaZzuXUww2MOGiR++GIv8ogoOMiKkYdXdD3JyctTarFRTKpXYsWOH6uehQ4eqbvv5+QGoGnK6ZcsW5rEKhQLbt29HaGgowsPDYWv7dEf948dVM3IkEgnTt1wul2P9+vVISkrSmLNUKsX8+fMxb948HDt2jDmura2NTp06AQDS09UXt0ydOhUAkJWVxRTfgaoV+tWDXE1MTODj46Mxh8aq/n0tLS2ZnuPl5eVYvXp1q169XnPVfnX98lmIRCLVMNhTp04hPj4eMTEx4PF4VGBvRdpNgf327dtqP5uammLWrFlYs2YNNm3ahBUrVqiK5iUlJfjss89w6NAhtcfUvFpYczJvXWq+gNV+wSGkJZkb62LC0K5M/MKtZCSmsf3OCCGkLToZFI/ULLYf7qIJPWjbeAsYN9ABVqbs7BH/gEjVUFZCCGnL5AolfjvKXsiV6Glh9hgXDjLqmOb5ukJQqyd8WYUcB849+zBIQlrSG2+8AX39qp2Ua9euxccff4xbt24hISEBFy9exLx58xAcHAwAGDVqlFp74UGDBmHkyJEAgN27d2P58uUIDg5GcnIyQkJC8Pbbb6v6qy9cuFD1PABUBfDMzEx88cUXiI2NxcOHD3Hw4EG88sor2LNnj+q+crn6rhx9fX1V25rvvvsOW7duxf3795GcnIwHDx7gyy+/xLVr1wAALi7qr3++vr4YPHgwAOCnn37CypUrERYWhuTkZJw6dQqvvvoq4uLiAAAfffSRWs4vovr3vXPnDrZu3YqEhARERkZi586dmDBhAv7+++86f9/WQCKRqFb///TTT7h69SqSk5MRFxeH06dPY+XKlXU+trqQnpSUhK+++gpAVd/+6r8Twr128200LCxMdXvSpEn49NNPmYnHb731Fr799lv8/vvvAIAvv/wSo0aNUhXKa17pqm9YQzVdXV3VbZlM9kL5E/KiXh3pjL9DklBU+nSLVtX2/QgaZkMIafNyC8uxX8MXa/euphjyUsMXxcmLEwn5mDfODV/vuaUWT0gtwJU7KRjWh21XRgghbcm5kCQkpLI9hef5ukJcq00WaT42ZmKMG2iPgGuP1OJnbiRiwtCusDEXc5MYIXWwtbXFzz//jBUrVqCgoAB//fWXavBpTQMGDMCGDRuY+MaNG7Fy5UpcvHgRf//9t1qhuNorr7yCd955Ry22aNEiBAYGoqCgALt27cKuXbvUjvfv3x+FhYWIjo5GQkICc861a9diwYIFSEtLwzfffKPxd7O0tGQKvzweDz/88APee+89XL16FQEBAarWJdVEIhE++ugj1Qr9pvD222/j1q1bqKiowDfffMPk7Ovri5s3byIrK0vj79safPLJJ1iwYAEeP36MxYsXqx3T1Ku+Wq9eveDg4IBHjx7hypUrAIDJkyc3a66kcdrNCvZJkybhiy++wMcff4wNGzYwxXWgapLyqlWrMHr0aABVQyRqvujVLJLr6Og0+JxC4dPrE9SLj3BNrCvC9FHdmfjt6Ezci9U8d4AQQtqKP05FoqyiVj9cHrB0CvXDbUmDPWzg1NmIie8+EwWprPWtFCKEkGdVXFqJXafZ9opdOxlitFcXDjLq2F4b3R26tXanyRVK7DpDLTBJ6zRgwAAEBARg6dKlcHFxgZ6eHoRCIczMzDBs2DB8++238Pf3h1jMXiDS19fHL7/8gs2bN2P48OEwNTWFUCiEsbExhg8fjq1bt2L9+vUQCARqj3NwcMCJEycwf/582NvbQ0dHB4aGhvDy8sKXX36JnTt3ol+/fgCACxcuMEVne3t7BAQEYOXKlfD09ISRkRGEQiFMTEzQq1cvrF69GgEBAejcmV1EIZFIsG3bNnz33XcYPHgwjIyMIBKJ0KlTJ0ybNg3Hjx/HzJkzm/BvGOjbty+OHj2KV155BZ07d4aWlpaqBc1PP/2E7777Dj179gQAHDlyBLm5uU36/E2hd+/eOHLkCPz8/GBubg6BQABdXV1069YNs2fPrvexEydOVN3W1dXFmDFjmjtd0gg8pVLZ4fb0RkVFqa70eHh44ODBgwCqpg2vXr0aQNWVwOrbdVEoFKpBp56envjzzz/rvX9T/OPOy8tj+rDfuHFDrR886bikMjne3HABmbnqfcecbA3xzbs+4POpCEUIaXuiE3Px/uYgJu47yB5vveLBQUYd2/24LHz0y3UmvniiOyb7OHGQESGEvLjfjt5HwNVHTHzDsiFwczDlICNy4NxD7DkbzcQ3rRiK7l3o+29FRYVawbRr166q4ZaEkPYnJSUFo0aNglKpxIQJE7Bp06YXOt/zvobk5uZi4ED1LglUl2xHK9gbo3v37qopw1FRUaqV6zWHJNQ1abmmmvepfSVRExMTkxf+Uz05mBBNREIB5o5zZeJxKQUIupuq4RGEENK6KRRK/GJiEgUAACAASURBVHb0PhOX6Ikweyz7ekeaXy8nc/R1tWTiB87FoLi0koOMCCHkxSSmFeL09UQmPqyPLRXXOTTJ2xEmBmyxxz8gEh1wnSAhpIMzNjZWFcCnTJnCcTaktg5ZYOfz+aoe65WVlSgsrBoCWXNoaVZWwy01ag5FrT2xmRCueL/UCY627AwB2r5PCGmLzt98jLgUth/unHGuMNCnfrhcWTDeDbU3RRWXSXHoQiw3CRFCyHNSKpX4/dgDZlizjpYAC8a7cZQVAQAdbSFmaRguG5GQg5uRGRxkRAghLevSpUu4d+8eEhMTsWHDBpSXl6NTp07MCnLCvQ5ZYFcoFMjLy1P9XH3129raWhWLj49v8DxpaWmq2+bm5k2YISHPj8/nYeF4dyaekVuqcWUOIYS0VsVlUuw6HcnEHWwMMGaAfcsnRFS6WBtgRF87Jn4iKAGZeaUaHkEIIa3T9ftpuB+XzcRfG90dpoa6HGREahrVzw6dLdme1TtPRUAuV3CQESGEtJyzZ89i+vTpGDNmDA4cOAAAeP/998Hnd8hybqvWLv6LZGRkYO/evXj77bcRExPT4P3Dw8Mhl1et5BWJRKq2K3Z2djAwMAAAJCcnIzub/aBV0+3bt1W3HR0dnzd9Qpqch7M5PF0smPiBczEoLpNykBEhhDTen39Ho6CYbTnyxuSeENBMCc7NHusCLaH6R0mpTIG9GvrlEkJIa1ReKcP2k+FM3NpMH5O8u3KQEalNIOBjvi+7kyA5oxjnbyZzkBEhhLQcBwcHiMViiEQiODs749tvv8W4ceO4Toto0C4K7OHh4Vi7di3++ecfBAQENHj/U6dOqW736dNHdeVHIBCgf//+AKpWtV+4cKHe89y6dUt1282Ntg+S1mXBeDfwatWfikorcZi27xNC2oDH6YUah815v9QJPRzNOMiI1GZmpIuJ3uwCg4u3k/HoCdvWhxBCWpsjF+OQlVfGxJdM6gGRsOEZW6Rl9He3gntXthf+vsAolFfIOMiIEEJaxtKlS3H79m2Eh4fj5MmTGD9+PNcpkTq0iwL7gAEDoKenBwDYt29fvSvPHz58iL1796p+njBhgtrxUaNGqW7v3r0bCoXmbWcZGRkICQkBAEgkEvTq1eu58yekOTjYGGJ4n85M/MSVeGTns18kCCGktVAqldiqoR+utpYAC/zYFliEO6+O6AaJnnovfKUS2HmKbe1DCCGtSUZuqcaFJ31dLdHPzYqDjEhdeDweFvixC9pyCytwPKjh1q6EEEJIc2sXBXZ9fX289tprAICioiK89957KCoqYu4XFhaGhQsXQiqtapHh6OiIyZMnq93H19dXNbA0JiYG/v7+Gp9zw4YNkMlkqscIhcIm+30IaSqzx7pAVGv7fiVt3yeEtHLB4Wm4F8teLJ82shvMjakfbmuiryvCa6OdmXhYdCbuxTQ8MJ4QQriy/UQ4KmXqi6mEAh5en9SDo4xIfVy6mGBwLxsmfvhCHAqKKzjIiBBCCHmqXRTYAWDZsmXo3Llqte7NmzcxatQofPLJJ9i6dSu2bNmCN954A7Nnz0ZOTg4AwMLCAr/99htTGNfW1sa8efNUP2/cuBG//fabqihfXFyMNWvWqNrMCIVCzJ8/vyV+RUIazcJYDxOHsv0jL9x6jMS0Qg4yIoSQ+lVI5dh2IoKJW5roYYqPEwcZkYb4DrKHpYkeE/c/FcHsQiCEkNbgbkwmbjxIY+KTvB3RyZwdqElah3m+rswMlrIKGfafe8hRRoQQQkiVdlNgF4vF2LNnD9zdq7aO5+fn4+DBg/jmm2+wefNmXL58GQqFAjweD2PGjMHx48dVBfnalixZAk9PTwBV29S//fZbDB48GFOnTsXgwYNVk3uBqn5INOCUtGavjugGsa5ILaZQAn/Q9n1CSCt05GIcMnNLmfjrk3pAS0T9cFsjkVCAueNcmXh8SgGC7qZykBEhhNRNJldg6zF2sKmxRBvTR7E7ckjrYWMuxtiB9kz8zPVEPMkubvmECCGEkH+1mwI7AFhZWeGvv/7Chg0bMHDgQBgYGEAkEsHMzAweHh5YsmQJTpw4gR9//BEmJiZ1nkdLSwu///47hg8frooVFBQgIiIC5eXlqtjs2bOxfPnyZv2dCHlRYj0tjdv3b0Vl4H4cbd8nhLQembmlOPRPDBP37G4BL3fqh9uaDX2pE5xsDZn4rjNRkMrkHGRECCGanb72CMkZbDvRBX7u0NMRaXgEaU1mjO4OXW31C+5yhRK7T0dxlBEhhBACtLvG4QKBAJMnT2Z6qzeWWCzGr7/+irNnz8Lf3x93794FAPD5fPTu3RtLly6Fj49PU6RMSLMbP9gBJ4MSkJmnPtzUPyAS36zwBr/WVktCCOHCjoAIph+ugF/VD5fHo9ep1ozP52GBnzs+/vW6WjwztxSnrydikjft9iOEcC+/qAL7AtlZRC5djDHM05aDjEhjGUm0MXV4N2am1NV7TzDlcR6c7Yw5yox7SiW1ZSOEPDt6zWha7WoFe3MYO3YsDhw4gNu3byMwMBA3b97Evn37qLhO2pS6tu/HJefj2r0nHGRECCHq7sdlaXw9mjC0KzpbSjjIiDSWRzdz9HGxYOIHzj1EcZmUg4wIIUTd7jNRKCmXqcV4PGDplF604KQNmeztCGOJNhP3D4joUAUjPl+9nCOX044xQsizq/2aUfs1hTQO/e09I7FYDHt7e4jFNPSGtE3evW3RtZOm7fuRkNZaMUoIIS1JLldg69EHTNxIoo2ZL3fnICPyvOaPd0PtzQZFpVIcvhDLTUKEEPKv2OQ8nAtNYuIve3WBU2cjDjIiz0tHW4hZY1yYeHh8Dm5GZXCQETeEQqFaQay4mPrQE0KeXc3XDD6fD6Gw3TU5aVFUYCekg+DzeVjo58bE03NKcebGIw4yIoSQKmduJCIpne2HO9/XjfrhtjEONoYY0ZcdIn/iSjyyarUpI4SQlqJQKLH16APUXtysryPUuMuTtH6j+9vB1oJd/LYzIBJyecdYPMTj8SCRPN3lV1BQgMrKSg4zIoS0FZWVlSgoKFD9LJFIqCXnC6LLE4R0IC85W6C3sznuxKgPN93/dwxG9rWDvi4VsgghLauguAJ7zrL9cJ3tjDQWaknrN3uMK4LupKr106+UKbAvMBrvzujNYWaEkI7qUlgKopPymPissS4wFLOtRkjrJxDwMX+8G77wD1WLJ2cU4Z9byXjZqwtHmbUsiUSiKpLJ5XIkJibC0NAQYrEYAoGACmaEEBWlUgm5XI7i4mIUFBSotYipebGOPB8qsBPSwSzwc8fd7y6preApKq3E4YuxmOfLrnAnhJDmtOdsNEo09Oemfrhtl7mxLiYM7YrDF+PU4v/ceoxJPo6wtzbgKDNCSEdUWi7FzoAIJm5nJYHvIAcOMiJNxcvdCq72JohKzFWL7z0bDe/enaCj1f7LHWKxGAYGBigsLARQVWTPzc1Fbm5uA48khJAqBgYG1A67CVCLGEI6mK6dDDHM05aJH7+SgJwC2r5PCGk58Sn5CAxOZOKj+tnB2c645RMiTebVkc6Q6KnvilIqgT9ORXKUESGkozp4PgZ5RRVM/I1JPSEU0NfhtozH42HRBHcmnltYjhNXEjjIqOXxeDzY2NjAwIAuXhNCGs/AwAA2Nja026UJ0CcKQjqgOWNdIRKq//OvlMqxV0ObBkIIaQ5KpRK/aeiHq6cjxLzx1A+3rRPrijB9FDug9lZUBu7HZWl4BCGENL3UrGIcvxLPxAf1soaHszkHGZGm5mJvgoE9rZn4oQuxKChmL6y0R9VFdltbWxgaGqoNPiWEtAypTIH0nBIkZxQhr7Cc63TqxefzYWhoCFtbWyquN6H2v2eKEMKwMNGD35CuOHqp1vb9m1Xb97tY0QoIQkjzCrqbymzpBoCZL3eHsUSHg4xIUxs/2B4nryYgM7dULe4fEIlvVnhTCyBCSLPbfiIcMrn6lVwtIR+LJvTgKCPSHOaPd0NIRDoUiqf/rcsqZDhwPgZvTO7JYWYtp3rgqUQigVKphEwmg0LRMYa9EsK1CqkMn20LQVaNz7zTRjm3ylkQfD4fQqGQiurNgArshHRQ00d2w7mQJBTX6H2s+Hf7/prFAzjMjBDS3snkCuw5w+6YsbUQY/zgrhxkRJqDSCjA3HGu+GbvbbV4XHI+rt5LhXdvtl0ZIYQ0lYiEHNyMzGDir4zoBksTPQ4yIs2lk7kYYwZ0wZnriWrxM9cfYcKQrrA20+cmMY7weDyIRKKG70gIaRInrz1GTHKRWiw0KhsTvJ05yohwgfYOEdJBifW0MG0k+4J/MzIDD+KzOciIENJRnAtJQlpOCRNfMqkn076KtG3eL3WCo60hE991OgpSmZyDjAghHYFSqdQ488HcWBdThztxkBFpbjNf7g4dLYFaTCZXYveZKI4yIoR0BMWllTh0IZaJ21rQ0NCOhr7FEtKB+Q1xgLmxLhP3PxkBZe3GyIQQ0gTKK2XYf+4hE+/tbA5PFwsOMiLNic/nYeF4dgBdRm4ps9KQEEKayq2oDI1tyOaMdYWOFm3ibo+MJTqYOoy9eBJ0NxUxj/M4yIgQ0hEcuRSHkhpdAYCqz7/jBztwlBHhChXYCenAtEQCzBnLDhOMTc7H1XtPOMiIENLeBVx9hNxCdujYPF83DrIhLcHD2Rye3dmLJ/vPxTBfSAgh5EUpFJpXLdtZSeDjSa2p2rPJw5xgJNFm4jsDImnxECGkyeUWluP4lQQmPrJvZ9haSDjIiHCJCuyEdHDDPG3hYMMONd19OgpSGQ3GIYQ0nbq2UA7uZQOnzkYcZERaygI/N9SepVRUWonDF9n/PxBCyIsIupuKR08Kmfjcca4Q0HDldk1XW4hZL3dn4g/is3E7OpODjAgh7dmBcw9RKVVveSgS8jHzZReOMiJcogI7IR0cn8/DAj92+35aTgnO3khs8XwIIe1XXVso54yjD6HtnYONIYb36czEj1+OR3Z+GQcZEULaI5lcgb1n2SHa3bsYw8vdioOMSEsb7dUFnczZ3sc7AyIgV9AqdkJI00jPKUFgcBITHz9Ycxte0v5RgZ0QAs/uFnjJ2ZyJ7z/3EKXltH2fEPLicgvLcSKItlB2ZLPHujBDbCtlCuwLZIthhBDyPOoaoj3f1w282ttoSLskFPAxfzzbAjMpvQgXbz3mICNCSHu092w0c9FOV1uIV0d04ygjwjUqsBNCAAALxrP9jwtLKnH4YhwH2RBC2psD5x6iopK2UHZkFsZ6mDCkKxP/5+ZjJKWx7RwIIaQx6hqi7dndAj2dzDjIiHBlQA9ruNqbMPE9Z6NRXinjICNCSHvy6EkBLt9JYeJThjnBUMzOgSAdAxXYCSEAAEdbIwzrww5+OnY5HjkFtH2fEPL8aAslqTZtZDeIdUVqMYUS2HkqkqOMCCHtRV1DtOf6squZSfvG4/GwUEMLzJyCcpzUsJuOEEIaY/eZKNSem2wo1sIkb3YhCek4qMBOCFGZM9YVQkGt7ftSOfYFsquBCCHkWe0NpC2UpIpYTwvTRzkz8VtRGXgQl81BRoSQ9qDOIdoeNnCypSHaHZGrgwkG9rRm4ocuxKKgmL0QQwghzyLyUQ5uRmYw8ekjnaGnI9LwCNJRUIGdEKJiaaIHvyEOTPx8aBIep9P2fUJI4yWmFeJyGG2hJE+NH+wACw07F/wDIqCsvRyIEEKeQZ1DtMdSG7KObJ6vK/h89d77peUyHPwnhqOMCCFtmVKpxK7TUUzc3FgX4wbZt3xCpFWhAjshRM30Uc7Q17B9/49T7BsJIYQ0ZPdp2kJJ1GmJBJgzjm3ZEJucj6v3nnCQESGkLatriPaofnY0RLuDs7WQYIxXFyZ++tojpGsYhksIIfW5HZ2JiIQcJj7rZReIhAIOMiKtCRXYCSFqJHpamD6SbdsQGpmO8Hjavk8IeXaRj3IQGpnOxGkLJfHpbYuuNoZMfNfpSEhlCg4yIoS0VXUP0e7OUUakNZn5cnfoaKkXvmRyJXZrWIVKCCF1USg0v250thRjeN/OHGREWhsqsBNCGH5DusLMiLbvE0KeH22hJPXh83lY4OfGxNNzSnH2RmKL50MIaZvqG6Kt6bMs6XiMDXQwZZgTE79yNxWxyXkcZEQIaYuu3ktFwpMCJj5nrCsEtVpRkY6JCuyEEIaWSIC549ielTGP83HtPm3fJ4Q0rO4tlN1pCyUBAPTuboHezuZMfP+5hygtl2p4BCGEqNt7loZok4ZN9nGEkYSd+7IzIJIWDxFCGiSTK7DnbDQT79bZSOMwZdIxUYGdEKKRj2dn2FsbMPFdp6No+z4hpF71bqHsQ1soyVML/NzBq7Xop7CkEocvxnGTECGkzUhMK8TlO+wQ7anDaYg2UaenI9LYMuh+XDZuR2dykBEhpC05H/oYadns3Ib5vm7g1f4gSzosKrATQjQS8HlY6OfOxNOySxAYnNjyCRFC2oxr957UvYVSQB89yFNdOxlimKctEz92OR45BWUcZEQIaSvqGqI9cSgN0Sasl726oJO5PhPfGRDB7IIghJBqFVI5/vz7IRN/qZs5PDTsxCQdF33LJYTUqXd3c3h0M2PitH2fEFIXmVyB3WfZ1eu0hZLUZc5YVwhrXXiplMqxL5D9MkMIIUA9Q7RH0RBtoplQwMc8X3b2R1J6ES7eSuYgI0JIW3DqagJyC8uZ+FxfVw6yIa0ZFdgJIXXi8XhYoGEVe0FxJY7Q9n1CiAa0hZI0loWJHiZoWHF6PjQJj9MLOciIENKa1TVE28JYF+MG2rd8QqTNGNjTGi5djJn43rNRqJDKOciIENKaFZdJ8dc/sUx8YE9rONuxryWkY6MCOyGkXk62RvDprWH7/hXavk8IUVfXFkqPbma0hZLUa9rIbtDXVV91qlACf5xii2iEkI6triHaM192oSHapF48Hg8LJ7CLh7ILynEyKIGDjAghrdnRS3EoLlPfuc/nAXPH0ep1wqICOyGkQXPGuTDb9ysqNRfSWpJSqUR5pYz6JhLSStS1hVLTlmxCapLoaWH6SGcmHhqZjvD4bA4yekqhqHqvIYRwr+4h2hIM70tDtEnD3BxMMaCHFRM/9E8MCksqOcjoKZlcgUpaSU9Iq5BXVI7jV+KZ+Ii+duhsKeEgI9LaCblOgBDS+lmZ6mP8YAfmDeZcSBImeTs2+g1GqVSiQipHWbkMpRUylJRJ/70tRWm57N8//96uqHG7Rqzs39tyhRK62gK87GWPhRPcIeBTCwpCuEBbKMmL8hvigIBrCcjKU98d5R8QgU0rvBvdYkiuUKK8ovZ7iqb3mX/fg8plKCln34/KKqqK6+bGulj26kvwdLFost+ZENI4dQ3RnjvOhT4Dkmc2z9cNoZEZUNRYpFNSLsPB8zF4fVKPRp9PJlegrPo7Te33nWf8TlNSLkOlVA4eD3DsZIj35/aFjZm4KX9tQkgjHDwXg4pK9QteQgEfM8d05ygj0tpRgb0BZWVl2L9/P86ePYu4uDjIZDLY2dlh3LhxWLhwIXR1dblOkZAWMX2UM86HJqGk/OkqPoUS2HrsAXwH2aOkTL1IUVohQ2mZVP3DZI2YoglXnZdVyHH8Sjz0dISYNcalyc5LCHl2tIWSvCgtkQBzxrriuz/D1OIxj/Nx6EIsrEz1axQnnr7nlPwbqy6Ml5TJUFYhRVlF064CzMorw7odwfj+/w1DF2uDJj03IaRhdQ3RdrYzwoAeNESbPLvOlhK87NUFZ28kqsVPXUuAs50RFEqoCt8Nfacp/bcw3lSUSiAupQCf/HYDW1YNh642lWwIaWnpOSU4G5zIxH0H28PCWK/lEyJtAr1a1+Px48dYsmQJEhMT1eIxMTGIiYnB8ePHsX37dtjasv2pCWlvDPS18OpIZ/xxKlItfjcmC3djsjjKSt3B8zEY2NMaDjaGXKdCSIdCWyhJUxnmaYtjl+Pw6In6cFNNAw25IJMr8cOBO9i4fCgEAuq0SEhLqmuI9jwaok2ew8yXu+Pi7WS1FaoyuRIb99zmMKunMnNLsftMFN6Y3JPrVAjpcPYFRkMmV18QqKst0NjOkJBq9M2gDhkZGViwYIGquC4SiTB8+HBMnToVnTtX9fdLTEzEggULUFhYWM+ZCGk/JgztCjNDHa7TqJNcocSPB+5ALldwnQohHQptoSRNhc/nYYEfO4CuNYlNzsfxKzQMj5CWVNcQ7Ze6mcOjGw3RJo1nYqCDKT5OXKdRr4CrCYh8xA70JYQ0n6S0QlwKS2Hik32cYCjW5iAj0lZQgb0Oq1atQmpqKgDAyckJp0+fxq+//oovv/wSgYGBmDp1KgAgOTkZ69ev5zJVQlqMtkiA2WNbd7uHuJQCHLvMrqQlhDQP2kJJmppndwu81MoLZnvPRuFJVjHXaRDSYdQ1RHuub+v+XEpatynDHGHUigtmSiXw44E7qKDBp4S0mN1noqCs1c1WoqeFyT6O3CRE2gxqEaPBuXPnEBoaCgCQSCTYtm0brK2f9vUTCAT47LPPEBYWhsTERBw7dgyvv/46nJxa9xVwQprC8L6d8XdIEqISc1/oPCIhH3o6Quhpi6CrI4S+jgh6OkLo6gihpy2E3r8/q/7331jN+4qEfKz68QqSM9SLHHsDo+HVwwq2FtSagpDm9uffD2kLJWlyiya644PNQSivfLGigq62ALra1e8nNd9Tnr7n6NeI6da+n44ISWmF+O/PV9W+bFXKFPjx4F2sf2sw+DRYkZBmVdcQ7UG9aIg2eTF6OiIsnODOzP5oLB4P0NMWQldHBH2dp99ZGvpOo1fzO5C2EGdvJME/IELt3KlZJfgzMLrV7+4ipD2ITsxFSEQ6E58+yhl6OiIOMiJtCRXYNfj9999Vt5cuXapWXK+mpaWFefPmYe3atVAqlTh06BA+/PDDlkyTEE4I+Dz87/UB2Pd3NGIf50Mo4DOFC91/PzjW/QFTCJFQ0CT5rHitNz7YHKRW+JDKFPjxwF189c4QKnwQ0oyS0gpx8XYyE6ctlORFOdgYYt3SQTh5NQEZuaVq7yG6NQrkNd9XahfNdbSFEDTBe4B7V1NMGNIVJ4L+P3v3Hd5U2f4B/Jsm3XvQRTel0JYWCmWUsodMkSmCUsWByhTHq/5e9ztUVBTBheBAVHhBBZWhDNkFCrQFChS6W7r3Xkl+fxQL6UnoSnua9Pu5Li7Cc07SG3jak9znee5btSxMXFIB9kWmYFq4d7u/BhFppqmJ9kNdfFcl6YZxoe4wkABHo2+ioqruLp9pGhLoDdcj1cS5iZFUK30A7hvdCycv3sT1tGKV8V+OJiK8vyt6u/OGElFHUSqV+HbvFcG4g40ppg736vyASOcwwd5ETk4OYmNjAQAymQxz5szReO6sWbPw9ttvo66uDseOHWOCnboNc1NDPHFf12i409fTDjNG9hI0WLyaUog9J5Nx70gfkSIj0n/cQkkdqa+XHfp62YkdBgBg0RR/nInLRk5hpcr4N7/HIdTfCU52LIdE1BE0NdEeP5hNtEl7xgxyx5hB7mKHAamBBCvnh+CZtUdUdgcqFEp8vD0Ga58ZDUMZq/wSdYTo+DxcThT2PFh4Tx8YGWpncSDpN/50biImJqbxcVBQEOzsNH+wMzMzQ+/evQEAiYmJKC9nLU4iMTw0pS9c7M0F49/uvYLsggoRIiLSf5q3UPbmFkrSOybGMqyYN0AwXl0rx4YdMVA2vdNERFqhqYn2A/ewiTbpJ09nKzwwUTi/U7JKsfOwsFQSEbWfQqHEln3C1etujhYYFyr+zTfSDUywN5GYeHuFxIABwg9STfXt27fxcXa2MNFARB3PxEiGFfcLv19rmPgg6hBKpRJb9l4VjDtYm2DqcJbLIP3U368HJg3zFIzHXM/Doag0ESIi0m+ammhPC/dmE23Sa3PG9Ya3q5Vg/H8H45GSVSpCRET67dSlTCRmlAjGH5rsD6mUaVNqGc6UJjIzMxsfe3s3nySwt7dvfNxcgr2wsLDdv4qKitr+lyPSY0G+DpgS5iUYj72Rjz/PMPFBpE3R1/NwKTFfML5gUl9uoSS9tnh6IOytTQTjm3ZfRkFJlQgREekvTU20543vLVJERJ1DJjXAyvkhgl5S9XIl1m2PhlyuECkyIv0jlyuwdZ9w4ZCvmzWGBwv7MRJpwhrsTRQX324o4urq2uz51tbWjY+bS7CHhYW1PTAiatYj0wMQdTUH+cWqSY6vfruMUH9H2FubihQZkf5QKJTYoqYBUM8eFhjPLZSk58xNDbFsbn+8tfmMynhFdT0+++ki/rl4iFYa3RF1d5qaaM9iE23qJnzdbDBnrC92HFItC5OQXozdxxIxeyxvNBFpw8GodNzME5aVjZgawPd01Cpcwd5EZeXt5lV3Js81MTO7vT2xvr6+Q2IiopYxMzHE8nn9BeOV1fX4ZGcsS8UQaYGmLZSLpnALJXUPgwOcMWaQm2D8TFw2jsfcFCEiIv2jrom2lbkR7mMTbepGHpjYB26OFoLx7/dfw8089n8jaq+aOjl+/POaYDzY1wED/HqIEBHpMn4SbqKurq7xsYmJcAtwUzLZ7U0AvLtFJL5BfZ3UNiKJupKDo9FMfBC1B7dQEjV44r4g2KhZRfvFL5dQUl4jQkRE+kNTE+154/3YRJu6FSNDKVbND0HTNENtvQIfb4+GQsHFQ0TtsfdkMgpKqgXjEVP9md+jVmOCvYnWfhNJpbdrzRoY8J+TqCt4/L5+sLUUJj42/nIRRWXCCygRtQy3UBI1sDI3wlOzgwXjpRW12PjLJREiItIPGpto25hi6nCvzg+ISGR9vexw70gfwfiV5ELsPZUsQkRE+qGiqk5QggkAhvVzRh9POxEiIl3HGuxNGBvfTsqVlze/7erOc5pLsEdGRrY9sFuKioowderUdr8OkT6zNDPC03OC8d9volTGyyrr8MUvl/BSxGCRIiPSXTV1cmzjFkqiRuH9XTE8jH0T7gAAIABJREFU2AWnLmapjB+LuYmRIT0xrB93dRC1lqYm2gvv6cMm2tRtLZrsj7Nx2cguqFQZ/3bPFQwOcIaTnZmGZxKRJr8cTUBZZa3KmIEEeGiKv0gRka7jkusm7qy7np8vfHPX1J1NUW1sbO56rp2dXbt/2dratv0vR9SNhAW5YkR/YaPik7GZOHUxU4SIiHTb3pPJyOcWSiIVT80KhqWZsGTFZz/ForzJhzYiujtNTbTdHC3Ulv8j6i5MjGVYcf8AwXh1rRwb/hfDPlNErVRcVoPdRxMF42MGucPT2UqEiEgfMMHehKvr7YRcQkJCs+dnZ9+uD9ijB1fwEXUlT84KhqWZkWD8s58vCu5WE5Fm3EJJpJ6tlQkevy9IMF5YWoPNv8aJEBGR7tLURPshNtEmQrBvD0wO8xKMx9zIw4GzaZ0fEJEO23HoOqpr5SpjMqkBFk7qK1JEpA/4TqWJwMDAxsfR0dHNnn/+/PnGxz4+wtpoRCQeG0tjLJklTHwUl9Vg0+7LIkREpJvUbaGUcAslEQBg7CA3hPo7CcYPRqXhQnyuCBER6R6NTbTdbTA8iOWWiABg8fQAOFibCMa/+vUyCkqqRIiISPfkFlZi76kUwfiU4V4st0TtwgR7E8OGDWuspX727Nm71mHPyclBWlrD3WIvLy9YWFh0SoxE1HKjQ3piSICzYPzwuXScu5ojQkREukXTFsqx3EJJBACQSCRYOqc/TI2FrY027IhBZXWdCFER6RZNTbQfZhkyokZmJoZYNk9YKqaiuh6f7rzIUjFELfDDn9dQL1eojJkYSXH/eD+RIiJ9wQR7E9bW1ggNDQUAVFdXY/v27RrP3b17d+Pj4cOHd3hsRNR6EokES+cGw8xEmPj4hIkPomap30Ip4RZKojv0sDXFo/cGCsbziqrw7R5hTWkiuu3uTbQdRYiIqOsK9XdS25Pg7JVsHIu+KUJERLojLbsUf51LF4zfN7oXbCyNRYiI9AkT7GosXLiw8fGnn36K9HThN2Bubi42b97c+OcZM2Z0SmxE1Hr21qZ4bEY/wXh+STW++Z2JDyJNNG+h9OYWSqImJg3zRLCvg2B876kUXE7MFyEiIt2gqYn2w9MCRIiGqOt7/L5+apOBX/xyCcVlNSJERKQbtu6/BkWTjR6WZoaYNdpXnIBIrzDBrsbEiRPh7e0NACgvL0dERIRKrfVr167hkUceQXFxMQBg0KBBCAkJESVWImqZiUM8MKC3sBHxvsgUXEpg4oNIHW6hJGo5iUSC5fMGwNhIKjj28f9iUF1bL0JURF2bpibaYUEu8POwFSEioq7P0swIT88OFoyXVdZi465LIkRE1PVdTytC5KUswfi88X4wNzUUISLSN0ywqyGTybBmzRqYmpoCADIzM7Fw4ULcc889mDJlCu677z4kJjbUozUzM8Prr78uZrhE1AISiQTL5vWHidrERzSqa5j4ILqTxi2Uo7iFkkgTFwdzRKhp/puVX4Ef/ogXISKirk1dE20DCfDQZJYhI7qb4cGuCO/vKhg/HnMTkZcyRYiIqGtTV7LP3toEU8O9RYiG9BET7BoEBwfjq6++Qo8et1e8pqamIikpqfHPlpaW2LBhA/r06SNGiETUSs725mq3G2cXVGLrfmHtT6LuTOMWyjHcQkl0N9NG+KCvp3Dl7e6jCYhPLRQhIqKuSWMT7VB3eLCJNlGznpwVBEsz4crbz366iPImN66IurOY67m4qGbX+oJ7+sLYULgAj6gtmGC/i4EDB2Lfvn146qmnVBLtlpaWmDt3Ln799VeEh4eLGCERtdbU4d4I8LYTjP96PBHXUpj4IAI0b6GcO45bKImaIzWQYOX8EBjKVN9mK5TAuu0xqKuXa3gmUfeivom2ARbew9XrRC1ha2mCJTODBONFZTXY9OtlESIi6nqUSiW+3XtVMN6zhzkmDBY2DCZqKybYm2FpaYnVq1fjxIkTOHr0KA4dOoTTp0/jP//5D1xdhVuyiKhrM7iV+DBqkvhQKhtKxdTWMfFBpGkL5bQR3EJJ1BLuTpZYcI9wh2N6Thm2H7wuQkREXYumJtpTh3vBkU20iVps9EA3hPo7CcYPRaXj/LUcESIi6lpOXcpCQnqxYPzByf6QSpkSJe3hbGoFZ2dnuLm5QSaTiR0KEbVDzx4WWDhJuDoqPacc2w6wRi51b5q3UPbhFkqiVpg1xhe93KwF4zsP3UByZokIERF1HZqaaM9jE22iVpFIJFg2tz/MTIQ5ig07YlFZXSdCVERdg1yuwNZ9wtXrvdysER7MBbOkXUywE1G3NHN0L/i62wjGf/orAYkZwjvcRN2Bpi2Urg7mmDDYQ4SIiHSXTGqAVfNDIDWQqIzLFUp8tC1akFwk6i40NtEezSbaRG3hYGOKR+8NFIznF1fhGzW7Eom6i8Pn0pGRWy4Yj5gSAIMm78+I2osJdiLqlqS3Eh8yqeqFVaFQYt12Jj6oe9K0hfKhKdxCSdQW3q7WmDu+t2A86WYJfjmSIEJEROJT30TbCLNGs4k2UVvdM9QT/Xs7CMb3nUrBpUThzkQifVdbJ8cPfwp3pwf1ckBInx5qnkHUPvy0TETdlpeLFe5XsxU5ObMUPx2+IUJEROKRyxX4Ts3qdZ+e3EJJ1B7zJ/jBw9lSMP7DH/FIzykTISIi8cSnFqptoj1vfG820SZqB4lEguXzBsDYSFjOb/32GFTX1osQFZF49p5KQX5xlWA8Ypo/JBKuXiftY4KdiLq1ueP94OViJRjfduA6UrNLRYiISByHz6XjZp5wC+XDU7mFkqg9DGVSrJofgqbfRvVyBT7eHg1506W8RHpsi5obuQ7WJpgazibaRO3lbG+OiKn+gvGsggp8v/+aCBERiaOyug47Dgmbyg8NdEZfTzsRIqLugAl2IurWDGUGWDl/gNrEx/rtMUx8ULegaQtlv1723EJJpAV+Hra4T035i2upRfj9RJIIERF1Pk1NtB+4py+baBNpyfRwH/h7CROIvx5LxLXUQhEiIup8u48morSiVmVMIgEWTRHegCLSFibYiajb6+1ui1ljhImP+LQi/HY8UYSIiDqXpi2UD08N4BZKIi1ZOKkPXBzMBeNb9l5FVn6FCBERdR5NTbR79jDHhMHuIkREpJ8MDCRYOX8ADGWqqR6FEvh4ezTq6uUiRUbUOUrKa/DLUWGfmzED3eCpZuc6kbYwwU5EBGDBpL7o2cNCMP7d3qvIzBeWzSDSF3fdQqlmBRQRtY2JkQwr7x8gGK+tk2PDjhgoldwxRfqLTbSJOo+boyUWTuorGE/PKcf2A8L3fET6ZMehG6iqUb2RJJNK1H5PEGkT380QEQEwNpRi5fwBaLpYt7ZegfX/i4GCpWJIT+3iFkqiTtOvlwOmqak1fTEhH3+cThUhIqKOp6mJdi83awwPYhNtoo4wa3Qv+LpZC8Z3HL6BpJslIkRE1PFyiyqx52SyYHzyMC842wt3ERJpExPsRES3BHjbY/oIH8H45cQC7D+d0vkBEXWwkvIa7OIWSqJOFTHVHz1sTQXjX/0Wh7wiYakmIl2nqYl2BJtoE3UYqdQAK+eHQNrke0yhUGLd9mjUyxUiRUbUcbb9GS+Y2yZGUtw/0U+kiKg7YYKdiOgOi6b4w9HOTDD+ze9xyC2sFCEioo7DLZREnc/MxBDL5wlLxVTV1OOTnSwVQ/pFUxPtoF4OCPFjE22ijuTtao37JwgTi0k3S/DzX8IFFkS6LD2nDIei0gTjM0b1gq2liQgRUXfDBDsR0R1MjWVYqTbxIccnO2OZ+CC9oWkL5SRuoSTqcAP7OGLCYA/B+PlrufjrfIYIERF1DE1NtCOm+bOJNlEnmDfeD57OloLxH/+MR1p2qQgREXWMrfuvomlVVwtTQ8wa4ytOQNTtMMFORNREf78emDTMUzB+IT4Xh6LSRYiISPvUbaE0NpJivpqVTkSkfY/NCISdlbFg/Mtdl1BUWi1CRETaddcm2p5sok3UGQxlDaVimlZjqpcr8PH/YiBnnynSA9fTinDqYpZgfN743rAwNRQhIuqOmGAnIlJj8fRA2FsLt5Jt+vUyCpn40Ki6th6p2aU4czkLu48lYuv+qzgRe5Mr/7sYjVsoR/rA1opbKIk6g4WZEZ6e018wXl5Vh89/uShCRLpBqVSipLwG8amFOHIhA9sPxGPHoetIzmTTvq6GTbSJugY/D1vMHC1cxRufWoTfjieJEJFuUCiUyCuqwqXEfBw4k4qt+69i19FEFJXxs2BXo66Rtp2VCaap6a9G1FFkYgdARNQVmZsaYunc/vjX5jMq4xVVdfh0Zyz+uXhIt9za3JDYqEV2QQWyCiqQXVCJ7IKKxl+FpTVqnzcu1B3PPBDSLf/NuhqlUonv9qnfQjl7bG9xgiLqpob1c8GoAT1xLOamyvipi1k4GZuJ8P6uIkUmrnq5AnlFVY3XliyVa00lqmrqBc/58c94vLgoFEP7uYgQMTVVXKa+ifbYQe5sok0kgoWT++L05Sxk5leojH+37yqGBjrDxaF7lgesqZMju6ACOQWVDZ9t8iuQXViJrPwK5BRWqm0G+8uRG3h76Qi49rAQIWJqKvZ6HmJu5AnGF9zTB8aGUhEiou6KCXYiIg2GBDhjzEA3HLmgWg/3TFw2TsRkYmRIT5Ei61j1cgVyiyqRnV+J7MKKxjeYDb9XCJpitsThc+no5WaNGSN7dUDE1FIKhRJf/RaHyEvCLZRzx3ELJZEYlswKQsyNPMFK389/voggXwdYmRuJFFnHqqyuQ3aThEZ2fsPN27ziKihaWbagrl6BtT9ewEerx3TbRFFXUVBShTe+PM0m2kRdiLGhFCvnh+DlT0/gzo2ltXVyrP9fDP791HAYNK0jowcaFwcVVty6xqjesG3LzuTC0hq8syUK760cxQSuyK6lFOLd76IE4y4O5pgwRNjrhqgjMcFORHQXT8wMQsz1PBSXq67M/mLXRQT3doC1hbB+ri6oqKpD1p2rNe5YJZhfVClY3awNX/8Wh76edvDzsNX+i1Oz6uoVWLctGkejhQ0U7ayMMW2EtwhREZG1hTGenBWE97aeVxkvLq/Bl7sv4bmFg0SKrH0UCiUKS6tVEhlZdzxuekNBGyqr6/Hud1FYs3wkjJj0EEVGbhle3xiJ3CJhY9PJw7zgZGcmQlREBACBPvaYNtwbvzdpcn8pMR9/nEnFlDAvcQJrp8bFQY3J84bf27M4qDnJmaX4ctclLJ83QOuvTS0TdSUb72w5h9o64f/vQ5P7QiZlRWzqXEywExHdhZW5EZ6aHYx3tqjeGS8pr8XGXZfwwkOhIkV2dwqFEgUl1XckzlWT6WWVdZ0eU71ciXe3ROGjZ8fA0kw/V2R2VZXVdXj72yjEXBdunwSAByf7w8SIbwmIxDJyQE8ci76JM3HZKuNHzmdg1ICeGBzgLFJkd1dTJ0duobpt9Q3XnNp64db6jpaYUYJNv17GUjX17aljXU8rwhtfnkZZpfDmibmJDPdPZBNtIrFFTAvA2SvZgptgX/8Wh0F9HeFo2zVvglVU1am5Wdvw57bsetKGP06nop+PPcYMcu/0r93dHTybivU7YtX+v/d2t8GI/vq505y6Nn6aJiJqRnh/V4QFuQjKahyLvolRA3qKXu9VqVQiM78CcUkFiEsqwI30ImTlq68Z2NEkEsDBxhTOduaQKxS4klyocjy3qAof/RiNVx7tnjXsxVBcVoM3N0UiIUN9A8Dp4d6YyC2URKKSSCR4ek4wLifmo6Jatb74Jztj8ckL9jAXuYRTXb0c19OKcSW54VqTklWKghJxGr0ZyQzgZG8OZ3szJGeWIr9YNVG071QK+vnYY1SImyjxdUfnrubgnS1RqKkVriQ0MZLipYcHw9aSTbSJxGZqLMPyeQPw2sZIlfGqmnp8sjMWbzw+TPT36BVVdbiaUoi4pAJcTSlEWnaZ2ht3ncHcRAZnB3M42Zkh6koO6prcOP5kZyx6udnA3clSlPi6G6VSiZ2Hb2CLmqamAOBib44XIwbrZbkj6vqYYCciaoGnZwfjUkI+yqtUV35/+lMsAns5dGrtarlCiZTMEsQlF+BKUiHikgtQXKa+uWhHMDKUwtneDC725nC+leD4+3cnOzMYyhq25dfVy/HihhO4kV6s8vyzV7Lxy5FEzB7r22kxd1fZBRV4bWMkspo0tPrbQ1P64v7xfqJ/kCIiwN7aFI/f1w/rtseojBeUVOPr3+M6fRt6ZXUdrqUUIe5WQv16WpEgsdCRrC2MGq4tduZwdlC95thamjR+eE5IL8YL648Lbipv2BEDn57WcHNk0qOjHT6Xho+3x0CuZiWhlbkRXn98GMvDEXUhIX0cMXGIBw6cTVMZv3AtF3+dT8e40M5deFFUWt14rbmSVIjkrBKVOvEdSSJpuP663PGZxsXeHE72ZnBxMIeFqWHj++R9kSn4dGesyvOra+V4Z0sUPlg5CibGTK91JIVCiU2/XsZvx5PUHu/lZo3XHx/Gm7kkGolS2Vk/ukgbCgsLERYWpjIWGRkJOzs7kSIi6j4On0vDhz9GC8YnDvHAyvkhHfZ1a+vkuJFe3LBCPbkA11IKUdlkhaO22Vgaw9nODM4ODckNF4e/k+jmsLU0bnFCNqewEqvWHkFFkxsTBgYSvL00HAHe9h0RPgFIzCjGG5tOq735YiABls0bgHuGeooQGRFpolQq8frGSESrKef07yeHo79fjw772sVlNbdu3DZca5JvlnRIP46/SQ0kcLQ1u+MmbUNyw+XWSkEzk5bfuN5zMhmf/3xRMO7lYoX3V7EJXUdRKpX45UgCvv79itrjjnZmeGtJGHr2sOjkyIioOeVVdVi25rCgyaeFqSE+/cc42Fp1TJJSqVQiq6Ci4Vpza6GQpoUg2nLn4iCnJouE7lwc1JLY3//+PI5F3xQcGz/YHc88MFDbodMtdfVyrP3hAk7EZqo9PqB3D7z8yOBWvXegtmNeUj0m2HUMJzKReJRKJd7cdBrnr+UKjr21JAwhfRy18nUqq29vi7ySXNghqwZl0r8TG+YqyY2/ExumWlyBceZyFv799VnBuL21CdY9O0ZnG8V2ZbE38vCfr8+iqkZ4I8ZIZoB/LAoVvbQREamXW1iJ5e8fFjRlc7Izw/rnx2rl57NSqUROYeWtci8N15ubeeXtft2mzExkjasB79zt5Gxvjh42ppBqqQGZUqnEe1vP43iMMOnR0TfBuyuFQomvfovD7mOJao97u1rhjSfCYNdBSToiar+zcdn411dnBONhQS54+eHBWtnhKFcokZZd2ljK8kpyAQpLtb/z1sbCuOH6csfiICe7hs82rVkc1JzK6jo8+9ExtdfMVfMHYMIQLl7RtsrqOvzn67O4mJCv9vioAT3xzIKBMJSxqWlnYV5SPSbYdQwnMpG48oqqsOy9w4LEpaOtKTa8MK5NiY+isurGUi9xSQVIydTOqsG/awYKkxvmcLAxhbQTa9Nt/vUydh0Vfggf2McRrz8+jHXytOh49E2s/fE86uXCSWRhaohXHxvKnQNEXZymFdn3jvTBkplBrX49hUKJ9JyyxutMXFKBVuqnSySAvZVJY0JDtZSLOSzNDDutBFVldR1Wf3gUmWpWQq5eENLpJQ/0WV29Auu2ReNodIba40G9HPDPxUNE7xtARM17f+t5td/LL0aEtqlRZF29AgnpxbiclI8ryYW4mlwg6C3SFlIDCRzt1K9Cd7Y31+rioOakZJXiuXXHUFuneiPcSGaAD54ZDS8Xq06LRd8VlVbjjU2nkXRTfS+pGaN88Ni9/fhZspMxL6keE+w6hhOZSHzq6u8BDc0in5wdfNfn/r1q8M5VHDfz2r8t0lBmAD8PWwR426GfjwN83W1gZW7U7tfVlnq5Ai9/cgLXUosExxZN8cf9E/xEiEr//HY8CV/uvqS2bqWDtQneXBIGD2e+6Sfq6hQKJf7vs5OISypQGZdIgHeWjWj2Jlm9XIGEjOLGLfhXUwpQVll31+e0hI2FMQJ87BDobY8AH3t4OFnCqAuVX0nOLMHz646htsmuL2MjKT5YNQqe/PnXbpXVdXj72yjEqCljBDQ0hn9u4cAWl1wgInGVlNdg2XuHUVKu2kTUxsIYG14Y2+xO08rqOlxLLWosL3Y9tUjwM7gtXBzMEehtj0AfewR428HJzkxru5604eDZVEHPFADo2cMCa58ZxVIlWpCZV47XNkYip7BS7fFHpgVg9lhf9pISAfOS6jHBrmM4kYnEp1Ao8eoXp9RuU3tn2QgE+tirnJuWU9aYUI9LKhDUOmwLMxMZ/L3sbr3ptIefh02X/zCbV1SFVWuPoKxS9Q28gQT491PhCPJ1ECky3adUKvHdvqvYceiG2uPuTpZ4a0kYHGxMOzkyImqrzLxyrHj/L0GiomcPc6x7bqxKXfHqmnrEp95uSBqfVoSaWnnTl2w1JzszBPrYN/5ydTDv8h9k/zidig07hEkPdycLfLBqdKeuctQ3xWU1eHNTJBIy1K8knBbujSdmBnXqDjkiar/jMTex5rtzgvExA93w3IODVMZKymtwJbkAl5MaenYkZZZC0c6ttxIJ4O1i3XAD99ZnG10oL/Xhjxdw+Fy6YHxUSE88/+CgLn+97MpupBfhzU2nBTd+gIZeXivvH4Dxg7kzTSzMS6rHBLuO4UQm6hqy8iuw/P2/BFsDXRzMsWp+CK6lNJR8uZpciPIqLawatDS+tWKwYeWgl6u1Tn6APXc1B29uOi0Yt7U0xrrnxrDrexvI5Qps2BGLg1Fpao/7e9nh1ceGwtKs6+xoIKKW+fmvBHz9e5xgfMZIHwT7OjQkOJILkJhRArkWaot5uVghwNuuMaFub617N+WUSiU+/PEC/jovLHkwdpAbVi8YyKRHG2TlV+D1jZHIKlC/627RFH/MG9+b/7ZEOkipVOK/35zF6cvZgmOr5g+AgYHBrZ4dBcjIbX+/DpnUAL3dbRqvNX297GChgyWlqmvq8dzHx5CWXSY4tnRuf0wJ8+r8oPTAhfhcvP3NWVSrWShgbCTFSxGDEervJEJk9DfmJdVrd4K9T58+2oqlReLi4iCTdd+VJ5zIRF3H7mOJ2LT7coe8trO9GQK87dHv1htPFx1YNdhSW/ZeUbvSOtjXAW89OVwnbxyIpbq2Hu9uOYdzV3PUHh8a6IwXFoWqrHQlIt0hVyjxj/XHcD2tWOuvLTWQwNfdpnELvr+3nd7ciKuuqcez644iPUeYCFo+bwAmDWMTutZIzCjGG1+eRnG5sDGhgQRYNm8A7hnKf1MiXVZYWo2law6jQgsLg5oyNZair6cdAnvZI9DbHr09bPXmvWl6Thme/eioIBlsKDPAeytGopebjUiR6aYjFzLw0Y8X1C4asDQzwuuPD0UfT+a+xMa8pHpayVTHx8dr42XuKiMjA+PHj+/wr0NE1FLTR/jgRMxNtXXFW0MiATydrRpWcdxapa6LqwZb6sFJfXEluVBQW/hiQj62H4jHwkl9RYpMt5RW1OKtzacRr2H+3TPUE0vnBHepepVE1DpSAwlWzg/BM2uPqG1c3BrGRlL09bRtSKj3soefhy1MjPRz0YqJsQwvRQzGs+uOCUrlbPzlIvw8bODtai1SdLol9noe/vPNWUFzd6Chod+LEYMxJNBZhMiISJvsrEzwxH398NG26Ha/lrWFEQJu3bwN9LaHt6uV3r4fdXeyxNK5/bH2hwsq43X1Cry75Rw+XD2aDZ9baNfRBGz+VbhrDwAcbU3x5pIwuDladnJURC2nn++qiYg6wd+Jj5UfHEG9vOXNfGRSCXzdGrZFBvjYI8DLDhZ6smqwJaRSA7zw0CCsWntEUFdv24F4+HvZIaSPo0jR6Ybcokq8vjFS4zbd+RP98OCkvnqz64GoO/N0tsL8iX3w/f5rrXqepZmRSrkXn57WkOlpgkMdD2crLJ0TjA9/VE0W1dYr8M63Ufhw9Wg2oWvG8eibWPvjebU3dyxMDfHaY8Pg7929V6sR6ZNxoe44FnMTF67ltup5jnZmCPS+XT/dzdGiW70HHTvIHXFJBfjjdKrKeFZBBT7+XzReihjcrf49WkuhUOLbPVfw85EEtce9XKzwxhPD9HoBGukHvUuwv//++/jyyy9bdK6TkxOOHTt213Oqqqqwbds27N+/HwkJCaivr4eHhwemTJmCxYsXw9SU3+RE3Zm7kyUWTuqDLXuvajzHxKhhW2SAT0PJl94eNnq7arCl7K1N8fyDg/DaxkjcWahMqQQ++OE81j07hm+iNEjNKsVrGyPVNsuVSIAnZwVjWri3CJERUUeZO643Tl3MRHJmqcZzHGxMb5V7aUhyuDlawqCbl9waF+qBy4kFOHBWtUdFZn4FNuyIxQsPsQmdJr8ebyiDp66YqIO1Cd5cEgYPZ6vOD4yIOoxEIsGyuf2x/L3DqKrR3Cjbw9ny1q7bhhXqPWz5nv2JmUG4nlYkuE6fupiF308k496RPiJF1rXVyxX4eHu02r4pABDoY49XHh2qkzX6qfvRuwzPxYsXtfZaaWlpeOKJJ5CSkqIyfv36dVy/fh27d+/G5s2b4ebmprWvSUS6Z/YYXxSX1eDX40kAGlYN/p3gCPDufqsGW2qAnyMemNgHP/6pWmaspLwW7209j/88NVxvt5O2VVxSAf711Rm19TFlUgM8/9AghAe7ihAZEXUkmdQA/1w8FP/+6gxSsho+vLs5WjSuTg/0toejnZnIUXZNT84Oxo304sZ/t78dj7mJQB973pBsQqlUYsveq9h5WNgrBWhIrL35RBgcbJhQI9JHjrZmePXRYXj72yiUVdZCaiBBLzdrBPo4INDbDv7e9rAy7z47b1vK2LCh+eYzHx4VlNT66rfL6ONpCz8PW5Gi65qqaurxzpYojTsmwoJc8PyDg2CkJ/X6Sf9ppclpZ9Zgv1uTU4VCgdDQUFRUVMDNzQ2jR4++62u0KNHmAAAgAElEQVRaWVnhmWeeUXssJycHCxYswM2bNwEAhoaGGDFiBGxtbREVFYX09HQAgLu7O37++WdYWXXOCg42EyDqusora1FVI4e9tUm3XzXYUnKFEq9vPIXYG/mCY/PG90bE1AARouqaIi9l4b2t51BXLyxHZGYiwyuPDkVQLwcRIiOiziKXK1BaWQupgQETHK2QkdvQhK7pikyZtKEJna87m9ABDSsJN+yIwaGodLXH/b3s8NpjQ7tVWTui7qq2To7SilpYmBrCxFjv1mV2mOMxN7Hmu3OCcUdbU6x7dgx/ft5SUl6DNzedxo109U3cp4R54cnZwZDyM3WXxLykenr1kzIhIQEVFRUAgAkTJuDll19u82s9//zzjcl1X19ffPbZZ/Dw8AAAyOVyvPLKK/j555+Rnp6O//73v3jnnXfa/xcgIp1mYWYECy4gbBWpgQTPPTgIqz44gqKyGpVjOw7dQIC3PUL9nUSKruvYH5mCz36KhULNLXE7K2O88UQYG/YRdQNSqQFsLU3EDkPnuDlaYvm8AXhv63mV8Xq5Au9sicJHz47p9tvPq2vr8e6Wczh3NUft8aGBznhhUSiMuZKQqFswMpRyp0objBzQE3FJBdhzMlllPLeoCh9ti8Y/Fw/p9qXJcgor8doXp5CZX6H2+IOT+2L+BL9u/+9Eukev9t7HxsY2Pg4MDGzz6xw4cABnz54FAFhaWmLTpk2NyXUAkEqlePPNN+Hl5QUA2LVrFxIS1DdkICKiu7O1NMELi0KhboHC2h/OI6+oqvOD6iKUSiV+/DMen+xUn1zv2cMca1aMYnKdiKgZo0LcMGW4l2A8p7ASH2+PRjs39eq00opavPL5KY3J9UnDPPHyw4OZXCciaoHHZgTC10343vxMXDZ2HU0UIaKuIzmzBC98fExtct1AAiyb2x8PTOzD5DrpJL1KsN9Zf71///5tfp07m6Q++eSTcHFxEZxjZGSEiIgIAA0JkJ07d7b56xERdXdBvRzw4GR/wXhZZR3WfBeFermwLIq+kyuU+Oyni/jhj2tqj/t52ODd5SPhxLrLREQt8viMfvDpKUx6RF7Kauyj0t3kFlXixQ3HEZ9apPb4/Il+WDa3P3uiEBG1kKFMihcjBsPcRFgw4ps9V3A1uVCEqMR3KSEfL31yQrBrGQCMZAZ46eEhmBzm1fmBEWmJXr1TiomJAQDY29vD09OzTa+Rk5PTuBJeJpNhzpw5Gs+dNWsWDA0btpMeO3asTV+PiIgazB3XGwP7OgrGr6UW4ds9V0SISDy1dXK8uyUK+yJT1B4f2NcR/3kqHNYWxp0aFxGRLjO61YTOTE3S4+vf4nAttXslPVKySvHCx8eRkVsuOCaRAE/PCcZDk/25kpCIqJWc7c2x6oGBgnGFQok130WhpFyYZNZnJ2Mz8drGSFRW1wuOmZsa4q0nhyMsSLiwlUiX6E2Cvby8vLFMy+DBg9v8On8n6QEgKCjorkX6zczM0Lt3bwBAYmIiysuFb06JiKhlDAwkeHbBQDhYC+sL7zqaiNOXs0SIqvOVV9XhtY2RiLyk/u87LtQdrz46lA2niIjawMXBHCvnhwjG5Qol1nx3DmWVtSJE1fnikgrw0objKCytFhyTSQ3wYsRgTB3uLUJkRET6ISzIBfeN6iUYzy+pxtofL0Chrv6jHtpzMhnvatiRbG9tgneXjUCgj70IkRFpl94k2C9dugSFouEb1tbWFu+99x5mz56NYcOGITg4GOPHj8eqVauwb9++xvPUSUy8XRNrwIABzX7dvn37Nj7Ozs5ux9+AiIisLYzxj0WD1XaM/2hbNLIL1DfD0RcFJVV4+ZMTiEsqUHt8zlhfPPNACGTcqk9E1Gbhwa64d6SPYDyvqAofdoOkR+SlLLz6xSlUqFlJaGYiw1tPhiE82FWEyIiI9MvD0wLQx9NWMH7hWi5++uuGCBF1HqVSia37ruLzny9CXZsTN0cLrFkxEp4uVp0fHFEH0Jvlb9HR0Y2Pf/zxR8HxjIwMZGRkYP/+/fD398eaNWvg5+cnOC8zM7Pxsbd386s27O1v32nLzs6Gr6+vxnMLC9u/7bSoSH19RCIifeHvbYeIqQH4+vc4lfGKqjq8+905rFk+AoYy/Wu0lp5Thte/jNTY1PXx+/qpXQVDREStt3h6IOJTC3E9rVhlPOpKDn45koA543qLFFnH2heZgs9/Ut84287KGG88EcbG2UREWmIoM8A/FoXimbVHUFZZp3Js676r6Otlh6BeDiJF13HkcgU+/eki/jyTqvZ4H09bvPbYMFiZG3VyZEQdRy8T7EBDeZfRo0fD3d0dtbW1uHLlCnbt2oWqqipcvXoVjz76KHbu3AlnZ2eV5xUX336T7era/MoNa+vbb0CbW8EeFhbWkr8KEVG3N2tML8QlFeDsFdWfqwnpxfjqtzg8OStYpMg6RnxqId7cdEZtaQKZVIJnHhiI0QPdRIiMiEg/NSQ9BmPV2iOoqFJNemy5lfTQpy3rSqUS2/6Mxw9/xqs93rOHOd5cMpyNs4mItMzR1gyrFwzEW5vPqIwrlMD7W8/ho2fHwNZSWCJTV9XUyfHed+dwJk59fmxwgBP+sSgUJkZ6k44kAqAnJWIUCkVj7XQTExOsXbsWO3fuxIoVKzBz5kzcf//9eOONN3DgwIHGVet5eXl4//33Ba9VWVnZ+PjO5LkmZma334TW1wu3WRIRUetJJBKsXhACR1tTwbHfTyTjROxNEaLqGOeu5uCfn59Sm1w3NZbitceGMblORNQBnOzMsPoBYT12xa167PrShE6uUOLTny5qTK77edjg3eUjmVwnIuoggwOcMWessNpBYWkNPvj+POR6UpqsrLIWr35+SmNyfeIQD/zzkSFMrpNe0osEe319Pd5++2288sor+PzzzzFt2jS15/Xo0QObN2+GqWlDwmbPnj3Izc1VOaeu7vYKFhOT5u8iymS3fzBIJMKawURE1DYWZkZ4MWIwZFLhz9aPt8cgM1/3G0sfikrDv746g5paueCYtYUR/vv0CIT0cRQhMiKi7mFoPxfMGqMu6VGND74/r/P12Gvr5Hh3SxT2R6aoPT6oryP+81Q4rC2MOzUuIqLuZtEUf7U7o2Jv5ON/B9TfANUleUVVeHHDCVxNUV8aed743lhx/wBI2UuK9JRezGwjIyNMmDABixYtarYMi6OjI+bNmwegYeX7oUOHVI63Nkkuld6uA2xgoBf/nEREXYafhy0evbefYLyqph7vfnsOtXXCxLQuUCqV2Hn4Bj7aFq02eeNsb4Y1K0bC191GhOiIiLqXiKn+8PeyE4xHX8/DjkPXRYhIO8qr6vDaxkhEXspSe3xcqDteeXQoTIy5kpCIqKNJpQZ44aFBsLYQ1h3/8UA8Yq7nqnmWbkjLLsU/1h9Dek6Z4JhEAiyZGYSIqQFclEp6rVu+m+rX73ay5tKlS1iwYEHjn42Nb6/eKC9vfnXknec0l2CPjIxsTZhqFRUVYerUqe1+HSIiXTF9hDfikgpw8mKmynhSZgm+3H0Zy+b2FymytlEolNj862X8ejxJ7XGfntZ44/FhsLXSn1qMRERdmUza0IRu5QdHBOW6fvjjGvy97RDs20Ok6NqmoKQKr2+MRGq2MNkBAHPG+uLhaUx2EBF1JntrUzy3cBBe/zISyjvW2CiVwAffX8BHz46GvbWwRGZXdjW5EG9tPo3yJv1MgIbr67MLB2LkgJ4iREbUubrlkmtbW9vGx/n5+SrH7qy73vSYOnc2RbWxuftKQzs7u3b/ujN2IqLuQCKRYMX9A+Biby44tj8yBUcuZHR+UG1UVy/HB9+f15hc79/bAW8vDWdynYiokznYmOK5BwcKxhVK4L2t51FUWi1CVG2TnlOGF9Yf15hcf/y+fnhkeiCT60REIgjp44j5E/oIxovLa/De1vOQyxUiRNU2Zy5n4ZXPT6pNrpsay/DGE8OYXKduo1sm2AsLb9eEUipVt+a7uro2Pk5ISGj2tbKzbzdv6NFDt1a2EBHpCnNTQ7wYEQpDmfCy9cmOGLXbEbuakvIavLXpDI7FqG/QOnJAT7z++DCYmRh2cmRERAQAg/o6Yd743oLx4rIavK8jTeguJebjxQ3HkVdUJTgmk0rw/IODcN+oXiJERkREf3vgnj4I9nUQjMclFeD7P66JEFHrKBRK7DuVjP9+cxa19cIbAjaWxnh7aTj692aOjLoPvSgRc+HCBRw9ehSZmZl47733mj0/Nja28bGTk5PKscDAwMbH0dHRzb7W+fPnGx/7+Pi0JFwiImqDXm42eGJmED7dGasyXl3b0MDt/VWjumRH+utpRdhzMhnHY26iTs0bUACYMdIHj83oBwMDriYkIhLTg5P64mpKIS4nFqiMX0zIx49/XsNDk/1Fikyzuno5TsZm4veTyYhPLVJ7jqmxFC8/PISNs4mIugCpQcMNz1Vrj6CorEbl2I5DNxDgbY9QfycNzxZPWWUtDpxJw77IZGQXVKo9x8XBHG8tCYOzmt3HRPqs62Ui2uCrr77CgQMHAABPP/30XRPdNTU1OHjwYOOfQ0NDVY4PGzYMBgYGUCgUOHv2LMrLy2FhYaH2tXJycpCWlgYA8PLy0ngeERFpx+RhnricmI9j0aqrwFOzy/DFz5ew6oEQkSJTVVsnx4nYm/j9RDJupBff9dyHpwVgzlhfbtUnIuoCGprQhWLVB0dQXK6a9PjfwesI8LbHwC6SpM4rqsK+yGT8eSYVJeW1Gs+zsTDG648PY+NsIqIuxNbKBC88FIpXPj+Jphuk1v5wAeueHYMetl2jHntCRjH2nEjGsegMtSvW/+brboPXHxsGG0tjjecQ6Su9KBEzfvz4xsfr16+/67kbNmxAbm5Dd2YLCwuMHTtW5bi1tXVj0r26uhrbt2/X+Fq7d+9ufDx8+PBWx01ERK0jkUiwbG5/9OwhvKF5MCoNB8+mihDVbTmFlfjm9zgs/tef+PDH6Lsm1w0MJFg1PwRzx/Vmcp2IqAuxszLB8w8OQtMfzQ1N6M6joERYfqWzKJVKxF7Pw3+/OYvH//Mndhy6cdfkurO9Gd5dMYLJdSKiLijI1wELJ/cVjJdV1mLNd1GoF7Eee129HH+dT8fzHx/D6g+P4mBU2l2T6yF+PfDfp8OZXKduSy8S7NOmTWusf75371588803gnMUCgU++ugjbNy4sXHs8ccfV2lq+reFCxc2Pv7000+Rnp4uOCc3NxebN29u/POMGTPa81cgIqIWMjMxxEsPD4aRoVRw7LOfLyE1q7RT41EolLgQn4t/f3UGS/57AD/9lYDSCs3JDqBhq/4ri4dgwhCPToqSiIhao79fDyyYKGxCV1pRizXfnev0JnSV1XX47XgSlq45jFe+OIXIS1mCFY9N+brbYM2KkXB14C5bIqKuat44P7U7o66lFuHbPVc6PZ7cokps2XsFi//1J9b+cEFj6bE7jR3khlcfGwZTY70okkHUJnox+42MjPCvf/0LTz/9NJRKJd5++23s3r0b4eHhsLa2RmFhIf766y8kJyc3Pmf69Ol46qmn1L7exIkT4e3tjeTkZJSXlyMiIgLvv/8+Bg0aBAC4du0ann32WRQXN6xMHDRoEEJCukZZAiKi7sDLxQpPzw7Cuu0xKuO1dXK8/W0UPlw9usPf4JVX1eFwVBr2nExGZn5Fi55jaWaIiUM8ce9IHzjYdI0tn0REpN79E/vgSnIhYm7kqYxfSS7Ed/uu4pHpgRqeqT2p2aXYczIZR86no6pG3qLn+LhaY2q4N8YPdodMqhfrqYiI9JaBgQTPLhyIVWuPoKCkWuXYrqOJCPSxx7B+Lh0ag1KpxMUb+fj9ZBLOxmU3ewP3b4P6OuLekT4Y2MeRO3Kp25MolcoWfuuo16dPH8THx2srHo0yMjIwfvx4xMXFQSZTnzTZu3cvXn31VZSXl2t8HSsrKyxfvhwRERF3/QFw8eJFREREoKrq9hZQT09PSKVSJCUlNY6ZmZlh27Zt6NNHuMKlIxQWFiIsLExlLDIyEnZ2dp3y9YmIupIPf7yAw+eEu4xGh7jhuQcHdsgbveTMkoZkx4UM1NS2LNnh62aNaeHeGBniBmM1K++JiKhrKi6rwaq1f6GwtEZw7NXHhmJIgLPWv2a9XIEzl7Px+8kkQbNVTWRSCYYHu2JauDf8veyY6CAi0jFXkgvw8qcnoWiS3TY3NcRHq0d3SNPQyuo6HIpKx95TycjI1ZxHaxrPxCEemDLcizukuinmJdXTixXsf5s6dSpCQ0OxdetWHD58GKmpqZDJZLC1tUWfPn0wYsQI3HfffS1qRhocHIyvvvoKK1euRF5ew6qV1FTV2r6WlpZYt25dpyXXiYhI1dOzg5GQUYy07DKV8aPRGejXyx6Tw7y08nXq6hU4fSkLe04lIy6ppckOA4wc0JDs8POwZbKDiEgH2Vga44WHQvHPz4RN6D681YTO0c5MK1+rqLQa+0+nYn9kCgpLq5s9HwDsrU0wJcwL9wz1hK2ViVbiICKizhfgbY+Hp/rj699Vy8JUVNVhzXfn8O7yETCUaWehzt+7o/46l47qFi4Y8nG1xrQR3hgV0hMmRnqVSiTSCr1awd4RysrKsGnTJvz000+NiXZLS0tMmjQJy5Ytg6ura6fFAvBOERFRU+k5ZXj2o6OCN4eGMgO8t2Ikerm1vbFbQUkV9kem4o/TKSgqE65eVKeHrSmmhHlh4hBPNvkhItITOw5dx5a9VwXjfTxs8fayETCUta0Ui1KpxJXkQuw9mYxTlzJRL2/ZR7NgXwdMDffGsEBnSFkGhohILygUSvz76zOIupIjODZ9hDeenBXc5teulytw+nIW9pxMbtXuqPDgnpgW7o2+XlwwRA2Yl1SPt52aYWlpidWrV2P16tXIzs5GfX09nJ2dOzXJT0REmrk7WWLp3P5Y+8MFlfG6egXe/e4cPnxmNMxNDVv8ekqlEpeTCrDnZDJOX8qCvIVFCAf07oFpI7wx2N+JyQ4iIj0zZ2xvxCUV4Py1XJXx+LSGJnSP39evVa9XXVOPo9EZ+P1EMlJa2Jzb1FiKsYPcMTXcG57OVq36ekRE1PUZGEiwekFDPfa8oiqVY7+fSEY/HweE92/dIs/C0mr80crdUQ7WJpg8/NbuKEvujiJqCa1kibtLiRRnZ+3XWCQiovYbO8gdcUkF+OO0aimvrPwKrP9fDF6MCG12xUVVTT2OnE/HnpPJSG1SckYTMxMZxg/2wNThXnBztGxz/ERE1LX9nfR4Zu0R5DdpQrf7WCICfewQFtR80iMzrxx7TiXj0Nk0VFTXt+hruztZYNpwb4wNdYeZSctvGBMRke6xNDPCi4tC8dInJwS7mtZtj4Z3T6tma5//vTtqz8lknLqY2eIFQ8G+DpgW7o2h3B1F1GrtTrB3RnkYIiKi5jwxMwjX04qQnKm6EvDkxUzsOZmM6SN81D4vPacMe08l4/C5dFS2MNnh6WyJaeHeGDPIHabG3NFERNQdWFsY4x+LBuPlT08IkhXrtkXD29VabRM6uUKJ81dzsOdkMi7E5wqOq2NgIMHQQGdMC/dGsK8Dt+UTEXUjfTztsHh6IL7cfVllvKqmHu9+ew7vrRwJI0NhPfbqmnocuZCBPSdbtztqXGjDgiEP7o4iajNmBYiISC8YG0rxUsRgPPPhUVTVqCbKN/96GX4etvDzsAUAyOUKnL2Sgz0nkxB7I79Fry81kCAsyAXTwr0R6GPPZAcRUTfk722Hh6cF4Kvf4lTGK6rr8e6WKKxZMbKxCV1JeQ0OnE3DvsgU5BZWtuj1bSyMMWmYJyaHecHBxlTb4RMRkY64d6QPLicVIPJSlsp4UmYJvtx9Gcvm9m8ca/PuqHAfjB3kxt1RRFrABDsREekN1x4WWHH/AKz57pzKeL1ciXe/O4e3loThZGwm9kWmIL+4Sv2LNGFnZYxJw7wwaZgn7K2Z7CAi6u5mju6FuKQCnInLVhlPyCjB5l/jMC7UHXtOJuN4zE3U1Sta9Jr+XnaYGu6N8GCXxgQ9ERF1XxKJBCvnhyA5swTZBao3afdHpiDA2w5mxjLsOZmM6Ot5LXpNAwMJhvVr2B0V1Iu7o4i0SaJUKltWjIm6BHbrJSJq3uc/X8Sek8nteo1AH3tMC/dGWJALZKxBSEREdyivrMWqD4+2eGW6OkaGUowZ6IZp4d7w6WmtxeiIiEhfJGQU44WPj6Ne3rIbturYWBpj0lDujiLtYF5SPa5gJyIivfPYjEDEpxYiIaOkVc8zNpJi7CB3TAv3hpcLaxASEZF6Frea0L244bigCV1zXOzNMTXcCxMGe8DCzKiDIiQiIn3g62aDJ2b2w2c/XWz1c/297DAt3BvDg11hKOOCIaKOxAQ7ERHpHUOZFC9GDMYza4+0qA5hzx7mmBrujfGhHjA3ZQ1CIiJqnp+HLR69tx827rrU7LkSCTCorxOmj/BGiJ8jDAy4LZ+IiFpmSpgXLicW4HjMzWbP5e4oInEwwU5ERHrJ2d4cqx4YiP9+c1btcQMJMDigoQZh/949mOwgIqJWmz7CG3FJBTh5MVPtcUszQ0wc4okpw73gbG/eydEREZE+kEgkWD6vP5JuFuNmXoXacxp2R3ljwmB37o4iEgET7EREpLfCglxw/wQ//O/g9cYxK3Mj3DPUE1PCvOBoZyZidEREpOskEglW3D8AN/PKkZJV2jju62aNaeHeGBniBmNDNi0lIqL2MTMxxIsRg/F/n55EeVUdgIbdUaH+TpgWzt1RRGJjgp2IiPTaoin+6ONpi/jUIng5W2FoP2cYMdlBRERaYm5qiHeXj8ChqHRUVNchxK8H/DxsIZEw0UFERNrj7WqND1ePxtHoDBgbSjGsnwt3RxF1EUywExGR3hsS4IwhAc5ih0FERHrKzMQQ9470ETsMIiLSc8725pg/oY/YYRBRE2wjTERERERERERERETUBkywExERERERERERERG1AUvE6BiFQiEYKyoqEiESIiIiIiIiIiIi6i7U5SDV5Sq7GybYdUxJSYlgbOrUqSJEQkRERERERERERN1ZSUkJHBwcxA5DVCwRQ0RERERERERERETUBkywExERERERERERERG1ARPsRERERERERERERERtIFEqlUqxg6CWq6+vR2pqqsqYtbU1DAz0815JUVGRoMb83r17YWtrK1JE1FVwblBLca5QczhHSBPODWoJzhNqDucINYdzhFqC84Sa0xlzRKFQCPpDenp6Qibr3m0+u/ffXgfJZDL06tVL7DBEZWtrCzs7O7HDoC6Ic4NainOFmsM5QppwblBLcJ5QczhHqDmcI9QSnCfUnI6YI929oak6+rnsmYiIiIiIiIiIiIiogzHBTkRERERERERERETUBkywExERERERERERERG1ARPsRERERERERERERERtwAQ7EREREREREREREVEbMMFORERERERERERERNQGTLATEREREREREREREbUBE+xERERERERERERERG3ABDsRERERERERERERURswwU5ERERERERERERE1AZMsBMRERERERERERERtQET7EREREREREREREREbcAEOxERERERERERERFRGzDBTkRERERERERERETUBkywExERERERERERERG1ARPsRERERERERERERERtIFEqlUqxgyAiIiIiIiIiIiIi0jVcwU5ERERERERERERE1AZMsBMRERERERERERERtQET7EREREREREREREREbcAEOxERERERERERERFRGzDBTkRERERERERERETUBkyw6xGlUonq6mqxw6AuiHODWoLzhFqC84Q04dygluA8oeZ0tTkil8tRW1srdhh0C+cHtRTnCjWHc4S0SSZ2ANR2crkcv/32G3bt2oWrV6+ipKQESqUSJiYmCA4Oxpw5czBjxgwYGDR/H+XkyZPYtm0boqOjUVxcDBsbGwwfPhxLliyBr69vm+LbsGED1q9fj1mzZuGdd95p8fPKy8uxfft2HD58GImJiSgrK4NMJoO9vT369euH6dOnY+LEiZBIJG2Kq62KioqwdetWHDp0CKmpqQAAHx8fzJw5EwsWLIBM1vpvp7y8PMyePRu5ubk4dOgQ3NzctBIr54buzY0RI0YgLy+vRV/vqaeewurVq9sVM8B5oovzBABOnz6NvXv34vz588jMzIREIoGnpydGjx6NBQsWwMnJSatx6+s8USqVOHToEA4cOICYmBhkZ2fDyMgIPj4+GD9+PObPnw9ra+s2xdQevNZwbmjCaw3nSUvo4rWmq82RsrIyfP/99/jzzz+RnJyMyspKAICNjQ2GDh2KiIgIhIaGtui16uvr8csvv+DXX39FfHw8qqqq4OrqivHjx2PJkiWwsbFp0etoky5dZwDOj86mq9cagHOls/F607XmyJ0SEhIwb948VFZWIj4+vtXP1xZdu960l0SpVCrFDoJar7CwEEuXLkV0dPRdzxsyZAjWr1+v8QeuQqHAq6++ip07d6o9bmhoiLfeeguzZ89uVXwHDx7E8uXLoVQqW/WBJT4+HkuXLkVGRsZdz+vfvz/Wr1+v9USSJrGxsVi2bJnGNwoDBgzAF1980aoLW3V1NR555JHG/0Nt/XDg3NC9uZGZmYmxY8e2+Gtq440o54luzpNnn332rv9nRkZGWLFiBZYsWdLumAH9nSfXrl3D6tWrkZSUpPEcCwsLvPLKK5g1a1arYmoPXmuEODca8FrDedISunit6WpzJDY2FitWrEBOTs5dz1u0aBH+7//+765JmMLCQixZsgSXLl1Se9zBwQEbN25EYGDgXb+WNunSdQbg/NDF+SHGtQbgXNHVucLrjXbmyJ0KCwuxYMECpKSkAIBoCXZdu95og/SNN954Q+wgqHWqqqrw4IMPIi4uDgBgYGCA0aNHY+7cuRg9ejSkUmnj3aGbN2/i8uXLmDlzptrVmk1/MJqJ77YAACAASURBVAQEBGDUqFGwsrJCZmYm5HI5Dh8+jL59+8LHx6dF8Z04cQIrVqyAXC4HAPj7+2PChAnNPi8vLw8LFixAdnY2AMDNzQ2jRo1CSEgIXFxcIJfLUVJSAgDIycnBwYMHMW/ePBgaGrYorraKj4/H4sWLUVhYCAAwMzPDuHHjEBQUhIKCAlRWViI7OxtRUVGYOXMmpFJps69ZW1uLpUuX4uzZs41jDz/8MKysrNoVK+eGbs6NEydOYP/+/QCAUaNGYcSIEQgODtb4a+jQofD29m5z3JwnujdPUlJSMH/+/MZkjoGBAQYNGoSRI0fCxcUFxcXFqKqqglwuR2RkJLKzszF+/Ph2xa2v8+TChQuIiIhAbm4ugIY3wcOHD0dYWBgcHBxQUFCA2tpa1NbW4uDBgzAwMMCQIUNaFFN78FrTgHNDiNcazpOW0MVrTVebIzdu3MBDDz2EoqIiAA03SSZPnoy5c+di8ODBKCgoQEFBAQDg4sWLd/2/LS8vx+LFi3H58uXGv1tYWBiGDBkCuVze+H+yf/9+TJkypd0/l1tCl64zAOeHrs6Pzr7WAJwrujhXeL3R3hy5U2lpKRYvXowbN240jq1YsaKF/0rao2vXG61Rks558803lX5+fko/Pz/ljBkzlFevXhWc8//s3XdYU9f/B/A3CVM2CuJAsDhQEeseXa5W60Kr1j3qt47W2qWtHdZabWutnVpnrduqVdxaF84qoq0VcaGi4AAUkL1J7u8PftwSkkAIN2Twfj2Pz5Pce3PuSfiQg5977uds2rRJPKZJkybCnj171I7Zvn27uL958+bCzp07VfafOHFCaNGihdCkSROhffv2QkpKSrl9279/vxAYGKhy7pkzZ+r0vj766CPxNd99952gVCrVjgkPDxeef/558bjly5fr1La+8vPzhRdffFE838iRI4WkpCRxf3Z2tjBhwgRx/6JFi8ptMy0tTRgzZozKZ9SkSRPh/v37le4vY8M8Y+Obb74Rj9P0M5Ma48S84iQ/P18YNGiQeExwcLBw9+5dlWMyMzNVfq5NmjQRNm7cWKm+W2KcpKWlCV27dhVfM378eCEhIUHlmOTkZGHatGkqbR87dqzctiuDYw1jQxuONYwTXZjrWGNKMVJYWCgMGDBAbOedd94RkpOT1Y55//33xWNatGih9Tv1ww8/FI979tlnhcjISJX9P/30k7h/9OjR5X5WlWVu44wgMD7MNT6qeqwRBMaKucUKxxtpY6RYXFyc0K9fP7Xv5KpmjuONVLjIqZmJiYnB1q1bARTVLlqzZg0CAgLUjhs5ciR69eolPt+9e7fK/uzsbPz000/i848++ggDBw5UOeaFF17A5MmTAQBpaWlYtWqV1n4pFAr8/PPPeP/995Gfnw97e/sKvS+lUile6fbx8cG7776r8Wphhw4dMGvWLPF5aGhohc5TUZs2bRKvZNavXx8rVqxAzZo1xf0ODg744YcfxBqbv/32m3gVUpOoqCi8+uqrCA8PB4AKf05lYWyYb2xEREQAAOzs7PSuIasrxon5xcnhw4fF2RZubm5YtWoV/Pz8VI5xdHTE7NmzERwcLG5bsmQJlEqlXv221DjZsmUL4uLiAAB+fn5YunSpWpkgDw8P/PDDD+jYsaO4bfHixRU6T0VxrGFsaMOxhnGiC3Mca0wtRnbt2oUbN24AAF5++WX88MMP8PDwUDlGLpdj/vz58PLyAgAUFBTgwIEDam1dvXpV7Ke1tTWWL1+OwMBAlWPeeecdcTbi+fPncerUKbV2pGRO4wzA+DDn+KjKsQZgrJhjrHC8kS5Gil24cAFDhw7FzZs3IZfLDX6HdlnMbbyREhPsZmbjxo0oLCwEAMyaNUvtF7Gk3r17i49L14jcvXu3eGtr48aNMWrUKI1tjB49WgzgHTt2iOcubdSoUVi6dCkEQUCbNm0qtEgUULT4QfGCDs2bNy/zFpGSA3VGRkaFzlMRSqVS5Qtx+vTpcHJyUjvO1dUVQ4YMAVB069G+ffs0tnfs2DEMHToUd+/ehY2NDebPn4+goCDJ+svYMM/YKCwsxLVr1wAALVq00Guhj4pgnJhfnBT/QQgU1eCrVauW1nNOnTpVfJycnCz+8VZRlhonf/zxh/j4jTfegIODg8bjrK2tMWXKFPH5tWvXkJqaWqFz6YpjDWNDG441jBNdmOtYY2oxsm7dOgBFSZ3PP/9ca61bW1tbdO/eXWt/AGDVqlUQ/n+Zs6FDh2qti/z666+Lj7dt26bxGCmY2zgDMD4A84yPqh5rAMYKYH6xwvFGuhgBii7+jx07FomJiXBycsLy5cvFxHxVM8fxRkpMsJuZkSNH4scff8SsWbPwzDPPlHmso6Oj+Lj0L/XRo0fFx0OHDtX6S+3u7o4XX3wRQNEXWvGAWVrxIgPDhg3DunXryvzS0sTOzk58XLwYgzYlv1gMuaBBRESEuCCDh4eH+DloMmzYMPGxtqvGV69eRV5eHurWrYu1a9dWeCGu8jA2zDM2ileBB4DWrVtL3FN1jBPzipPCwkKVhXjKqw/s6+sLT09P8XnxTMuKssQ4SUhIwP379wEUzQrp2rVrmce3a9dOfCwIgljbX2ocaxgb2nCsYZzowlzHGlOKEUEQMHfuXMyfPx8LFiyAu7u73v0pLCzEiRMnxOfDhw/X2s7zzz8Pb29vAMCZM2fE9QCkZm7jDMD4AMwzPqp6rAEYK4B5xQrHG+lipNilS5egVCrRuHFjbN26Fc8//3yZbRqSOY43UjL8JUWS1FNPPaXz4k3R0dHi47p164qPBUHApUuXxOfl/QIGBQVh7969AIoWWNB0xah+/fr48ssv0blzZ536VpqTkxMaNmyIu3fvIioqCgcPHlS5elgsPz8fS5cuFZ9XZIXyiir5xd+lS5cyb7Px9fWFm5sbUlNTcfnyZY3HWFlZYcSIEZgxY4bGq3iVxdgwz9go+XmXvJ3cUBgn5hUnaWlpyM/PF583adKk3POWvG2u+D85FWWJcVI84wQAateuXe6K9ba2tpDL5eJ/UorvkJAaxxrGhjYcaxgnujDXscaUYsTKygpPP/00nn76aZ36U/JCfb169VT23bhxQ/xZe3p6aixDUMzKygotW7ZEQkICsrKyEB0drdNnX1HmNs4AjI/i85pbfFT1WAMwVorPay6xwvFGuhgpZmNjg6lTp2LKlCmwtbXVqV1DMcfxRkpMsFuww4cPi49L/kfi0aNHyMzMBFBU86q8VbubNWsmPtZ2xXDfvn1ab5PV1cSJE/HJJ58AAGbOnIm4uDj07t0bXl5eyMnJwb///oslS5aIK2gHBARg8ODBlTpnWUp+ueryBdi0aVOEh4eLK16X/jz+97//Vfozkgpjo3KkjI3iOoUymQyPHz/G9OnTceXKFTx+/BjW1taoU6cOnnnmGQQHB5f5h5UhME4qR4o4KVnqRiaTaZ0pUUyhUKgkgari9kBziZOSn6UuK9UnJCSozAAy1GfJsYaxoQ3HGsaJLqrDWFOVMVKejIwMhIWFic87deqksr/kz0OXGbwBAQE4cuQIACA+Pt4gSTFLHmcAxkdlVZexBmCsVBbHG9OKkWKffPKJyXwnW/p4Ux4m2C3U33//LV49ksvlKrdSPHz4UHxc3hcDAJXbZx89eqTxGCmCfvDgwcjKysJ3332H3NxcLFiwAAsWLNB47DPPPIOFCxeqlIWQWskvQl0+p5ILNyQkJKi9xlS+GBgblSdlbBT/LJRKpcrim8XS09MRFRWFtWvXYujQoZg1a1aVXJlmnFSeFHHi6uqKGjVqIDs7G0qlEvHx8ahTp47WNm7duoW8vDwARWVzDF2jzpzipPjWWaBoJmp+fn6Zv0vFiy8BQJ06dQxWTohjDWNDG441jBNdWPpYU9UxUp7169cjNzcXANCgQQO1WbqV/XkYgqWOMwDjQwrVYawBGCtS4HhTxFRipJgpfSdb8nijCybYLZBCocDXX38tPg8ODoaPj4/4vOQiSyVve9Gm5C2whvqyLjZy5Ejcv38f69ev13pMt27d8M0332i8NXfdunXiisX6aNWqlbhSdUU/p+JVkAHNXw6mgLFhWrGRnJyMe/fuiftsbW3x4osvIjAwEF5eXnj8+DFOnTqFsLAwKJVKbN26Fbm5ufj222/1fh+6YJyYTpwU30Z49uxZAEWrzr/xxhta2yhZ/qZ79+4G/aPE3OKkZs2a8PX1RWxsLPLy8nDw4EEMGDBA47GCIGDZsmXi8759+6rsN7U4MTWMDdOKDY410rDkODHVscbUYiQhIUFl8bY333xTbfZlZX8exUwtRkwR48O04sNUxxqAsWJKscLxRje6xIhUTC1GzBkT7BZo9erV4qwZZ2dnvP/++yr7S9aALBnQ2pT8EtO2sIIUsrKyMGXKFJw/f77M444fP46ePXtixowZaot9HD16tNzXlyU7O1v8cij5OZVXZxOous+pMhgbphUb//zzj/i4RYsWWLRokdoMtwkTJuDIkSN47733UFBQgN27d2PgwIHo0qWL3u+lPIwT04qToUOHin+ErlixAs8++yxatmyp9vrt27fj0KFD4vOxY8dW/A1UgDnGydChQ/Hdd98BABYsWIDWrVur/PFcbNGiRYiMjAQAWFtbY+TIkSr7TTFOTAljw7Rig2ONdCw5TkxxrDG1GJk9e7Z4ztatW2PgwIFqx1S0TzVq1NDYJ1OMEVPD+DCt+DDVsQZgrJharHC8KZ8uMSIVU4wRc2WYSyBkNFeuXMHPP/8sPv/0009VVl0GVAO35IIR2lhb/3cdxsrKSoJeavbxxx+r/WLLZDLUq1dP7ZczIyMDn3/+OTZt2mSw/hQUFIiPdSkjUXIBB0N+TvpibEhHqtho1qwZvvvuO8yYMQO//fab1tvHX3zxRXz44Yfi8zVr1ujTbZ0wTqQjVZz06dNHXLA1JycHY8eOxcaNG5GRkYHCwkLcuHED8+bNw2effSa+pkOHDmjTpo0Ub0Mjc42TcePGiZ9LUlIShg0bhj179iAnJwf5+fmIiIjA9OnTVWbL9O/fX+uiQlLgWMPY0IZjDeNEF5Y61phajKxfvx4nT54EUJQQ+OqrrzS2YYpxa2njDMD4kJIljzUAY0VKHG+KmEqMmCJLHG8qgjPYLUhaWhrefvttMaj79euHQYMGqR1X0cCtii/ra9euqVyd9PLywkcffYQXXnhBXC04KioKS5YsUTnu+++/R79+/cSriBs2bJCsTxV9r6UX7DAljA3TjA0fHx+NM980GT58OJYvX47k5GScPXsWmZmZkq+kzTgxzTgBgIULF8LDwwO///47srOzMW/ePMybNw9WVlYQBEGtrRkzZujXaR2Yc5zY2tpi5cqV+PTTT3Ho0CEkJyfjgw8+EM9Z+rO0tbXFtGnT1Nox1TgxNsaGacYGxxrpWHKcAKYz1phajERERKiUsfj000/h7+9foXNrU/LnUbJPphojpoDxYZrxYWpjDcBYAUwzVgCON9oYMka0MdUYMUfm/w4IQNFCItOnTxcXX2jSpAnmzZun8diSC4kUr4RclpLHlPwFkNKOHTvExw0bNsTevXvRt29flYG2adOmWLRoEaZPny5uy8rKQmhoqEH6VPKKW0U/J1P6cmBsSM8YsWFrayuuBl98ZV9KjBPpSRkntra2+Pzzz7F7926MGDECTZo0gZubG1xcXNCgQQOVY/v27YtWrVpVsveamXucAEW3fS5atAgbN27EK6+8goYNG8LNzQ1ubm5qs0zHjx9v0JmnAMcaxoZ2HGsYJ7qwtLHG1GIkKSlJJfkyePBgDB06VOvxFf15ZGVlVbhPFWUp4wzA+DAESxxrAMaKIXC8KWIqMWKKLGm80QdnsFuIhQsX4vTp0wCKah0tXbpUpQ5XSSVLJSQmJpbbdsmFCnSpo6SP69evi48/+OCDMs8zadIkhIaG4tKlSwCKZqUaQsnaWklJSahdu3aZx1fF56QPxob0jBUb7u7u4mNdfj4VwTiRniHiJCAgAHPmzFHZtnHjRvGPQXt7e4POXjf3OCmpffv2aN++vcq2BQsWYPXq1QAAT09PTJ482eD94FjD2NCGYw3jRBeWNtaYUozk5+fjrbfeEheoCwoKUvtcSiv98yhPSkpKhfqkD0sZZwDGhyFY4lgDMFYMgeNNEVOJEVNkSeONPsz/EgFh27Zt4h/zNjY2+Pnnn8u8PatOnTri4+jo6HLbj4+PFx+XrkUllZKDRqdOnco9vuRCKCUXUpBSyVWPb9++Xe7xJVeH9vLyMkifKoqxYVmxkZycLD7WdOucvhgn5hsneXl5WLFihfh80qRJOq3Yrg9LiJOyJCYmYvPmzeLzGTNmGOR25dI41jA2tOFYwzjRhSWNNaYWI7NmzcK///4rnmvJkiUqsxg1qczPw1BxawnjDMD4sLT4MNRYAzBWzDlWON7oHiOmyFLGG30xwW7mTp06pXJla86cOeUmlxo0aAAXFxcAwP3798u9IlpyRXBD1X8qeSuJLnWbSt5mo8sqzvpo0aKF+Lj4y06bgoICREZGAii68ubh4WGQPlUEY8O0YyMnJwd79+7Fxx9/jJ07d5Z7zoKCApVZ2uVdDdYV48S046Q869evx+PHjwEU1b58/fXX9ext2SwlTsqyZMkS5OTkAADatm2LgQMHVsl5OdYwNrThWMM40YWljDWmFiM//vgjdu/eDQBwdHTE8uXLdfrPf0V+HhXtk77MfZwBGB+69ElfljTWAIwVXfqkL443phUjpsgSxpvKYILdjP399994++23xRWNJ0+ejCFDhpT7Orlcjg4dOgAoulp87Nixcs9TrHnz5pXosXbe3t7i4+Iv1LLcuXNHfGyo2pYlZ7ieOHECSqVS67GRkZHif6pKfqkYC2OjiCnHRkFBAWbOnIkdO3Zg69at5Z7z1KlTSE9PB1C0mnjLli317b6IcVLElOOkLGlpaVi5cqX4fNasWTqt1l5RlhQn2sTExGDbtm0Aivo9e/bsKjs3xxrGhjYcaxgnurCEscbUYmTNmjVYvny5eI4ff/wRAQEB5fanuN3i29yjo6MRExOj9diCggJEREQAAGrUqIGGDRvqdI6KMudxBmB8AKYfH6Yw1gCMFcD0Y6UsHG8qFiOmyNzHm8pigt1MhYeHY+LEiWJADhgwAO+9957Or+/Zs6f4eMOGDVoD/9GjRwgPDwdQtKhTUFBQJXqtXWBgoPj47NmzZR6blZWFU6dOASiaqdq9e3eD9Klp06aoX78+gKJbfA4fPqz12OIrjoDql4oxMDbMIzZcXFzQtm1bAEVXd4v7rUlOTg6++eYb8flLL71U6VvGGCfmESdlWbJkififkx49eqBr1676d1YLS4sTbRYuXCj+oT1q1Kgq/cOWYw1jQxuONYwTXZj7WGNqMbJq1SqV34M5c+bghRde0Lk/MplM5e+K9evXaz32yJEjYpm69u3bw8bGRufzVIS5jjMA48Nc4sPYYw3AWDGXWCkLx5uKxYgpMufxRgpMsJuhGzduYMqUKeKXaJcuXfD111/rVBahWJ8+fcQrojdv3sSaNWs0HrdgwQLxPwx9+vSBtbVh1sUNDg4WVw1ev349MjIyNB6nUCgwd+5ccX+/fv0kvaWsJCsrK4wYMUJ8/s0336gswlAsKioKISEhAIoGwn79+hmkP7pgbJhXbLz22mvi408//RS3bt1Sa+Px48cYM2YM7t27B6Bolsebb75Zqf4zTswrTjS5c+cOfv/9dwBFMfHpp59K1Ov/WGKcaBIWFoajR48CKKqR+M4771TZuQGONQBjQxuONYwTXZjzWGNqMbJt2zYsXLhQfP7WW2/h1Vdf1bkvxUr+PLZs2SIuql5SdnY2fvrpJ/H5gAEDKnweXZnjOAMwPswtPow11gCMFXOLFU043ugXI6bGXMcbqVTdX58kmTlz5qgsylenTh18++235b5u9OjR8PX1BVBUr3js2LFYtGgRgP9m3kyYMAE2NjbIzMzEt99+i/379wMArK2tMW7cOAO8myK+vr6YMmUKli5dirt37yI4OBjDhw9H48aN4e7ujuTkZNy5cwe7d+8WB2tPT0/MnDnTYH0CgKFDh+LXX39Famoq4uPjMXbsWHz//fdo3LgxAOD8+fOYPn06CgoKAAB9+/ZVKVVR1Rgb5hUb3bt3R+/evXHw4EE8fvwYr7zyCrp164amTZvC2toa0dHRKjMSbGxs8P3338PPz69SfWecmFecaDJ//nzxNW+++aZBytxYYpyUplAo8PXXX4vPP/nkkypZlLA0jjWMDW041jBOdGGuY40pxUhKSorKz9LZ2Rnp6en46quvyu1P6URQUFAQOnXqhHPnzkGhUGDSpEmYP38+evToAaCofu+HH36I2NhYAEV1hl966aVyz1MZ5jbOAIwPc4sPY401AGPF3GJFE443+sWIKTLH8UYqVoLUSzaTQT18+FDvcgbr169Hx44dxef5+fkYN24cLl68KG5zdXVF/fr1ER0djdzcXHH71KlT8fbbb+t8rvDwcIwdOxYAMGjQIJVbX7QRBAHLly/HL7/8Il7x06Zu3bpYtmxZldyCGxoaimnTpkGhUAAouirn7++PvLw83L9/XzzO09MTO3bsqNCCFGPGjMH58+fF8xTfTqMPxkYRc4uN/Px8fPbZZ9i1a1eZ52revDm++uqrSteVZZwUMbc4Keno0aOYOnUqAKBx48bYuXOn5LeCWnKclLR27VrMnz8fAPDss8/it99+q9DrpcSxpghjQx3HGsaJLsxtrDG1GNm5cyc++ugjvfoTFRWltu3+/fsYNmwYkpOTxW1169aFk5MTbt++LZYWkMvlWLVqVZXcHm8u4wzA+DDX+KjqsQZgrJhrrJTE8UZ3mmJEk+7du+Phw4cVeo2UzGm8kRJLxJiZ+Ph4ydqytbXFr7/+im7duonb0tLScPXqVZUvhlGjRmHatGmSnVcbKysrvPHGGzhy5AimTJmCtm3bolatWrC1tYWdnR28vLzw7LPPYtasWfjzzz+rrL5ljx49sGjRInFGkiAIuH37tsoXQ+3atbFy5UqjrvbM2DDP2LC1tcWCBQuwYcMGvPzyy/D09IS1tTXc3NzQpEkTDBs2DKtWrcLOnTsl+SOUcWKecVIsJydHnPFgZWWFOXPmGKTOoiXHSbHExEQsXrwYQNGMlM8//7zKzq0Jx5oijA11HGsYJ7owt7HG1GIkLi5Osv4ARTNJN2zYoLLYYFxcHG7evCkmxGxtbTF//vwqqz1rLuMMwPgo7re5xUdVjzUAY6W43+YWK8U43lgmcxpvpMQZ7AQAOHjwINasWSPW9ZLJZGjdujUmT55s9gstSCUxMRHLli3D3r17xcU3PDw8EBwcjMmTJ8Pd3d3IPTQMxkb5qmtslMQ4KR/jhHGii+oaJ4yN8lXX2CiJcVK+6h4nphYj+fn5WLduHbZs2YIHDx4AAOzt7dG1a1dMmzYNjRo1qvI+VecYYXyUrzrHR0mMlfJV91gxtRgxRdUtRphgJxWZmZlISkpCrVq1jFJD0hwolUrEx8dDJpOhdu3a4sKKlo6xUb7qGhslMU7KxzhhnOiiusYJY6N81TU2SmKclK+6x4kpxkhiYiJyc3NRu3Zt2NraGrs71TpGGB/lq87xURJjpXzVPVZMMUZMTXWJESbYiYiIiIiIiIiIiIj0YJmXDYiIiIiIiIiIiIiIDIwJdiIiIiIiIiIiIiIiPTDBTkRERERERERERESkBybYiYiIiIiIiIiIiIj0wAQ7EREREREREREREZEemGAnIiIiIiIiIiIiItIDE+xERERERERERERERHpggp2IiIiIiIiIiIiISA9MsBMRERERERERERER6YEJdiIiIiIiIiIiIiIiPTDBTkRERERERERERESkBybYiYiIiIiIiIiIiIj0wAQ7EREREREREREREZEemGAnIiIiIiIiIiIiItIDE+xERERERERERERERHpggp2IiIiIiIiIiIiISA9MsBMRERERERERERER6YEJdiIiIiIiIiIiIiIiPTDBTkRERERERERERESkBybYiYiIiIiIiIiIiIj0wAQ7EREREREREREREZEemGAnIiIiIiIiIiIiItIDE+xERERERERERERERHpggp2IiIiIiIiIiIiISA9MsBMRERERERERERER6YEJdiIiIiIiIiIiIiIiPTDBTkRERERERERERESkBybYiYiIiIiIiIiIiIj0wAQ7EREREREREREREZEemGAnIiIiIiIiIiIiItIDE+xERERERERERERERHpggp2IiIiIiIiIiIiISA9MsBMRERERERERERER6YEJdiIiIiIiIiIiIiIiPTDBTkRERERERERERESkBybYiYiIiIiIiIiIiIj0wAQ7EREREREREREREZEemGAnIiIiIiIiIiIiItIDE+xERERERERERERERHpggp2IiIiIiIiIiIiISA9MsBMRERERERERERER6YEJdiIiIiIiIiIiIiIiPTDBTkRERERERERERESkBybYiYiIiIiIiIiIiIj0wAQ7EREREREREREREZEemGAnIiIiIiIiIiIiItIDE+xERERERERERERERHqwNnYHqGIKCwsRGxurss3V1RUyGa+VEBERERERERERkWEolUqkpaWpbPP19YW1dfVOMVfvd2+GYmNj0adPH2N3g4iIiIiIiIiIiKq5AwcOwN/f39jdMCpOeyYiIiIiIiIiIiIi0gMT7EREREREREREREREemCCnYiIiIiIiIiIiIhID6zBbmZcXV3Vth04cADu7u5G6A2R+SpUKLFg3QVci3midxvN/Twwc1x7WMt5rZKIiIiIiIiILFtKSora2pCacpXVDRPsZkYmU0/kubu7w8PDwwi9ITJfW49E4WZ8PqztnPRu42Z8Po5HJGNYz6YS9oyIiIiIiIiIyDxoylVWN/wEiKjaSUjO29o1FwAAIABJREFUwubDUZK0tflQFBKSsyRpi4iIiIiIiIiIzAsT7ERU7RwMi4FCKUjSlkIp4GBYjCRtERERERERERGReWGCnYiqFUEQEHrhvqRthl64D0GQJmFPRERERERERETmgwl2IqpWElNzkJqZJ2mbqZl5SErNlbRNIiIiIiIiIiIyfUywE1G1ci8hwyDtxiakG6RdIiIiIiIiIiIyXUywE1G1kpevMEy7BYZpl4iIiIiIiIiITBcT7ERUrdjZyg3Tro1h2iUiIiIiIiIiItPFBDsRVSsNvJ0N0q6vt4tB2iUiIiIiIiIiItPFBDsRVSuebg5wc7KTtE03JzvUcrOXtE0iIiIiIiIiIjJ91sbuABFRVbKyskKP9j4IOX5bsjZ7tPeBlZWVZO0RERERERGZE0EQUFhYCKVSaeyuEJEWMpkM1tbWzF8YABPsRFTt9O7sh10no6FQCpVuSy6zQu/OfpXvFBERERERkRkRBAGZmZnIyMhARkYGk+tEZkAmk8HZ2RnOzs5wcnJisl0iLBFDRNWOd01HjHipqSRtjejVFN41HSVpi4iIiIiIyBwIgoC4uDg8ePAAaWlpTK4TmQmlUom0tDQ8ePAAcXFxEITKTzwkzmAnompqUNdG2HHiNrJzC/Vuo6V/TQzu1ljCXhERERERWR5BEJCYmoN7CRnIy1fAzlaOBt7O8HRz4OxJM1ScXE9PTzd2V4ioEop/h+vWrcvv4kpigp2IqqXD4bGVSq5bAZjySitYy3kjEBERERGRJgnJWTgYFoPQC/eRmpmntt/NyQ492vugd2c/3hVqRjIzM1WS63K5HK6urnBycoJcLmeijsgECYIAhUKBzMxMpKWlQaFQAChKsru4uMDZ2dnIPTRvTLATUbWTkJyFtfuvVaoNAcD2Yzfx/si20nSKiIiIiMhCFCqU2H7sFrYcjipz3aPUzDyEHL+NXSejMeKlphjcvTEnsJiBjIwM8bFcLoefnx9sbW2N2CMi0pWjoyPc3d0RExMjJtkzMjKYYK8kjlxEVK0olQIWbb2EvHyF2r7nnq4HNyc7ja+zs5GrbTv+zwPcvp8qeR+JiIiIiMxVdm4BZq84i00Hb5SZXC9JoRSw8eANzF5xFtm5BQbuIVWGIAgqCXZXV1cm14nMjK2tLVxdXcXnGRkZrMVeSZzBTkTVyp9hMYiMTlLb/nJnP7w5pBUEQUBSai5iE9KRV6CAnY0cvt4ucLCTY9L8UGRk56u8bvXeq/jqjS68DZKIiIiIqr1ChRJfrg5HZHSyXq+PjE7Gl6vDMXdyF85kN1GFhYUqC5o6OTkZsTdEpC8nJyc8efIEQNHCp4WFhbCxsTFyr8wXE+xliI6ORp8+fXQ+fs2aNejSpYsBe0RElZGQnIW1+66qbfd0d8D4fs0BAFZWVvB0d4Cnu4PacSN7NcWKnZEq2yKjk3D+agI6BtYxTKeJiIiIiMxEyLFbeifXi0VGJyPk+C0M69lUol6RlEom14GiEjFEZH5K/+6W/t2miuEl4TJERkaWfxARmQWlUsDiPy4hV0NpmLdffRo17Mu/Utu7sx/qeaovvrRm3zUUKjgYEREREVH1lZCchc2HoyRpa/OhKCQkZ0nSFhkW7+QlMk/83ZUWZ7CXISIiQnw8dOjQcuuKeXt7G7pLRKSnQ+dicPm2emmYXp188XQTL53asJbLML5fC3y15rzK9oeJmTgUFoO+zz4lRVeJiIiIiMzOwbAYnWuul0ehFHAwLAbj+7WQpD0iIiJDYoK9DMUJdjc3N3z55ZdG7g0R6evRk2ys0VAappabAyb0r9gf7R1beCPQvyaulLr19ffDUeja1geODqxZRkRERETViyAICL1wX9I2Qy/cx7i+zTnLkoiITB5LxGiRl5eHmzdvAgCaN29u5N4Qkb4EQcDiP/5FTp56aZhpOpaGKcnKykpjUj49Kx/bQm/q3U8iIiIiInOVmJqD1Mw8SdtMzcxDUmqupG0SEREZAhPsWly9ehUFBQUAgKCgICP3hoj0dehcLCJuqZeGeamjL9o01a00TGmNfdzRtW19te17Tt/B4yfZerVJRERERGSu7iVkGKTd2IR0g7RLREQkJSbYtShZf71NmzZG7AkR6evxk2ys3ntFbXstV/sKl4YpbczLzWBrrfoVWlCoxPoD1yvVLhERERGRucnLV79bVJJ2CwzTLlk2QRDwOCUbf19/hDMRcfj7+iM8TsmGIEizRgARUWmswa7FpUuXAAByuRxt27Y1cm+IqKIEQcDibZc0loZ569WnK10r3cu9BoJf8Me20Fsq20/++wADnn8KTRq4V6p9IiIiIiJzYWcrN0y7NoZplyxTQnIWDobFIPTCfY0li9yc7NCjvQ96d/aDd03Hqu+gxMaMGYPz58+rbbexsYGHhweaN2+Ovn37om/fvpDJTHN+7ebNmxEREYHx48cjICCgUm399ttv+Pbbb1GvXj0cO3ZMoh5Kq/hnNmjQIHzzzTfG7g5JiAl2LYpnsPv6+mL37t04fvw47ty5g8TERLi4uKBBgwbo3r07Bg4cCE9PTyP3lohKOxx+D5duJqptf7FDA7QNqC3JOYZ0b4zD4bFIy8xX2b5671XMf/MZLshERERERNVCA29ng7Tr6+1ikHbJshQqlNh+7Ba2HI6CQql9lnpqZh5Cjt/GrpPRGPFSUwzu3hjWctNMPFdGQUEBHj16hEePHuH48eNYt24dVq5cCQ8PD2N3TcW5c+cwZ84cAMCdO3fwxx9/GLdDRJXABLsGjx49Qnx8PICiX/K5c+eq7E9KSkJSUhIuXryIZcuWYcaMGRg5cmS57T558qTSfUtJSal0G0SW7nFKNn7bo14apqarPSYMCJTsPDXsbTCyVwCWhVxW2X71TjLOXUlA55Z1JDsXEREREZGp8nRzgJuTnaQLnbo42qKWm71k7ZFlys4twJerwxEZnazzaxRKARsP3kDErUTMmtARNewrd3ezKTh58iSAoju509PTERkZidWrVyM6OhqRkZF4//33sXbtWuN2shQHBwfxcZ06/L8zmTcm2DX4559/VJ7XrFkTvXr1QqNGjeDi4oJ79+5h7969uHv3LrKysvDFF1/A1tYWQ4YMKbPdzp07G7LbRISiPyiWbItATl6h2r63hj4Np0qWhimtV0df7D19Bw8eZ6psX7vvKto1qw0ba8ubEUFEREREVJKVlRV6tPdByPHbkrWZk1eIg2ExeKmTH+Qy3hlK6goVygon10uKjE7Gl6vDMXdyF7Ofye7t7S0+rlOnDpo2bYpevXqhf//+iI+PR1hYGG7cuFHpMixSatWqFdasWYPY2FgMGDDA2N0hqhTz/gYxkIsXL4qPg4ODcfToUXz++ecYNWoU+vfvj6lTp+LAgQOYOHGieNz8+fORmppqjO4SUQlHz9/DxajHatt7tPdBu2bSlIYpSS6X4TUNC6bGJRXV/yMiIiIiqg56dmggaXsFhUosDbmMGYtO4eY93slN6kKO3dI7uV4sMjoZIcdvlX+gGXJ2dsa4cePE58VrDZqSLl26YMSIEXB0NP+a+FS9McGuQXBwML766ivMmjULCxYsQI0aNdSOkclkmDFjBl588UUAQGZmJrZt21bVXSWiEpJSc7BKQ2kYDxd7vC5haZjS2jerjaBGtdS2bz4chcycAoOdl4iIiIjIFCiUAjb+ecMgbd++n4oZi07hl22XkJ6VX/4LqFpISM7C5sNRkrS1+VAUEpKzJGnL1Dz11FPiY04KJTIcJtg1aNmyJYYMGYIxY8aUu0jh1KlTxcdHjhwxdNeISAtBEPDLtkvIztVUGqYVnGrYGuzcVlZWmNC/BUp/XWRk52Pb0ZsGOy8RERERkbEJgoCl2yNw5nKcAc8BHDoXiynfHMWhczFQlrGQJVUPB8NiylzQtCIUSsFi7z7OzPyvlKmmyaNEJA3WYK+kpk2bwsbGBgUFBbh+/ToKCwthba35Yw0LC6v0+VJSUtCnT59Kt0NkaUIv3Mc/N9RLw3Rv54P2zb01vEJa/vXd0K2tD479fV9l+57Td/ByFz941+Qtb0RERERkedbtv4bD4bGStNXMzx1eHjVw8uJDjfszsgvwy7YIHA6PxRuvtEIjHzdJzkvmRRAEhF64X/6BFRB64T7G9W1e7iRLc7N//37xcWCg6l3dgiAgNDQU27dvR2RkJNLS0uDk5ITAwEAMHz4cPXv21NpuXl4eNm3ahEOHDiE6OhrZ2dlwdnZGs2bN8Oqrr+qct+revTsePnyIt956C9OmTSv3+MTERPz66684duwYEhIS4Obmhueeew7vvfee1tfs2LEDH3/8MQAgKkrzXQ+XLl3CsGHDAAChoaGoX7++2vv9/fffcfDgQdy+fRu5ublwdXVF8+bNy/2sKkOhUCAkJAR79+7FzZs3kZGRgRo1aqBx48YIDg7G0KFDIZfLDXJuqhgm2CtJJpPB1dUVSUlJyM/PR3p6Ojw8PDQeq207EVVOcloOVu2OVNvu4WKHicGGKw1T2piXm+GvSw+RX6gUtxUqlNhw4Do+GNOuyvpBRERERFQVth+7pXVh084t6+D81QSdZhnLZVYY0aspBndrDGu5DH26NMSykMuIiU/XePzNe6l4/+eT6N3JD2P6NIOzAe9WpcpJzcjDT1su4vLtJBSU+H+SqUnNzMOAGXska8/GWoagRrXw7vA2cHO2k6xdXeTm5uLmzZtYtWoVQkNDAQBBQUFo06aNeExWVhamT5+O48ePq7w2JSUFp0+fxunTp/HKK6/g66+/VrvokJGRgTFjxuD69esq21NTUxEWFoawsDCcO3cOc+fOlfR9Xb58GZMmTUJKyn9rMiQmJmLHjh04ffo0evXqJen5iiUlJeG1117DzZuqd6cnJyeLn9Wbb76Jd955R9LzFhQUYMqUKfjrr79UtmdkZODixYu4ePEiQkNDsWLFCshkLFBibEywV5JSqVT55RYE3qpGVJWKSsNEIEtDaZipQ542aGmY0mq5OWBg10b4o1RZmFOXHmLA80+hqS8vshERERGRZTh0Lgbr9l/TuO/14EAEP++PhOQsHAyLQeiF+0jNzFM7zs3JDj3a+6B3Z9U7Pps3rImf3nsB+8/exaaDNzSWgRQE4M+wGJy5HIfxfZujR/sGkMksa/axJfhpy0WNdxpbuoJCJf658Rg/bbmIORM7G/x8TZs21bqvefPmWLx4scq2Dz/8EMePH4erqyumTJmCTp06wdnZGfHx8QgJCcGuXbuwY8cOtGrVCsOHD1d57dKlS8Xk+quvvoohQ4bAw8MDSUlJ2LRpE/bu3YutW7diwIABaNdOmolmaWlpePPNN5GSkgI7Ozu88cYb6N69O6ytrXHkyBH88ssv2LRpkyTnKu2LL74Qk+sTJkxA37594ebmhqSkJKxevRqHDh3C8uXLERwcDD8/P8nOu3XrVjG53rNnT7z22muoXbs20tLSsGfPHqxbtw6nTp3C3r17ERwcLNl5ST9MsJfy6NEjHD16FGfOnMG7776LJk2alHn8lStXoFAoAAA2NjZwd3evim4S0f87/s8D/H39kdr2rm3ro0MLw5eGKW1wt0Y4fC5W7T8Qv+25igVvPWtxtxwSERERUfXzV8RDLNkeoXHfsBebIPh5fwCAd01HjO/XAuP6NkdSai5iE9KRV6CAnY0cvt4uqOVmr/XvY7lchgHP+eO5VvWwZt9VHP/ngcbj0rPyseiPSzgUHos3XgmCf32WjTElN2JTyj/Ighnz/Xt7e2PixIkYMmQI7O3txe1nz57F0aNHYWNjgw0bNqgk5318fNChQwdxdvamTZvUEuzR0dEAgEaNGmHevHkqr23VqhXu3r2LvLw83Lt3T7IE++bNm5GYmAgA+OyzzzB06FBxn7+/P7y8vMQyMFLKzMzE6dOnYWtri379+mHmzJnivvr162PhwoUIDw9Hamoqzpw5I2mCvfhzdnR0xM8//yyWo/bx8UFgYCDu3buHBw8eID4+XrJzkv6YYC/lypUr4m0sjRo1wvvvv1/m8SXrWbVt25a3ZRBVoSfpuVi5S700jJuzHSYNbGmEHgE17G0wsncAlpb6D8f1mCc4GxmPZ4LqGqVfRERERERSuBj1GN9v+geabt7u+0xDjOoVoLbdysoKnu4O8HR3qPD53F3s8f7Itnipoy+W77iM2IQMjcdFxabg/Z9O4uUuDTH65WZwcrCp8LlIegG+7tVyBnuxAN+qmYR58uRJ8fG0adNw+fJlFBYWqiXXAWDfvn0AikqQDBgwoMx2b926hczMTDg5OYnb/P39cfLkSSQlJSElJUVloqlMJkNISIgUb0nFiRMnAAC1a9fGoEGD1PYPGjQIq1evxq1btyQ9r5OTEy5duqR1f8myMSWrW0jB37/oQmVubi7i4uLQoEEDlf3Lly+X9HxUOcwGl9KpUydxZeXff/8dSUlJWo+NiopSuQWlf//+Bu8fERURBAFLtkUgK6dAbd/UIa2MWofxpQ4N4FPbWW37un3XTLruIBERERFRWW7EPMHXa8+jUKGeXX+hdX1MGtjSYHdsBvrXwk/vd8XrwYFwsNM8V1ApAPvP3MWUb44i9MI9KHWo/06G9e7wNmgb4AUb6+qVfrKxlqFtgBfeHd6m/IMl4O3tLf6bPHkygKLa4X/88YfasdoW+tREEAS1vNhrr70GNzc3pKamon///vj888+xefNm/P3338jOzq7cG9Hi7t27AIBmzZqJM7lLsrKyUqkxbwjx8fHYv38/fvzxR0ydOhXPPfcchgwZgtTUVADSl4x+5ZVX4OvrC4VCgcGDB+Pjjz/G+vXrERYWhvR0zetTkPFwBnspjo6OGDZsGNasWYOMjAy8++67WLZsGZydVZNlFy9exFtvvYWCgqLknr+/PwYOHGiMLhNVSycvPsD5awlq219oXR+dAusYoUf/kctlmNC/Bb5YdU5le3xyFg6cvSveMktEREREZC5i4tMxZ9U55OUr1Pa1a1Yb745obfAa6NZyGYKf98dzT9fD6j1XcfJfzWVj0jLz8dOWf3HoXCzeGByEhnVdDdov0s7N2U7yGuSCIGDsnEMa6/rry83JDuvn9LKIkp49evRAo0aNcPv2baxatQrDhw+Hre1/E9AyMoruAmnfvj2+++67cturWbOmynMvLy9s374dK1euxLFjx7BlyxZxn42NDZ555hlMnToVQUFBEr2jolItAODp6an1mNq1a+vdfvFnosmNGzfw1Vdf4fz58+I2mUwGX19fDB8+HKdOnUJcXJze59amRo0a2Lx5M1auXIkjR45gx44d4j65XI62bdti0qRJeO655yQ/N1UcE+wavPXWWzh69Cju37+PCxcuoGfPnnjppZfg4+OD/Px8XL58GadPn4ZSWTQT1cvLCytWrNB4FY2IpJeSnosVOzWUhnGyw6RBxikNU1rbAC883dgTl24lqmzfcjgKPdr5VOniq0RERERElZGQnIXZK85qvHu0xVM1MXNsO1jLq26GsoeLPWaMbotenXyxbMdl3H+kOTl2PeYJ3v3hBPo++xRG9QqAI8vGWAQrKyv0aO+DkOO3JWuzR3sfi0iuA0Wfz8SJEzFz5kw8evQIISEhGDFihLi/eAJpZmYmvL31W7fMx8cH8+bNw7x585CYmIjbt2/j8uXL2LVrF06cOIG//voLq1evRseOHSV5Tw4ODsjIyMCTJ0+0HlNWBYry3LhxQ+P2u3fvYuTIkcjKykKzZs0wevRoBAYGomHDhrCzswNQNNPcEAl2oOjixscff4yPP/4YKSkpiI6OxpUrV7Bv3z6cP38e58+fx7fffstFTk1A9bpHR0dOTk7YuHEjWrRoAQBITU3FH3/8ge+//x6LFy/GyZMnoVQqYWVlhV69emH37t3w8fExcq+JqgdBELBkewQyNfxx/+aQILg4mkbi2srKChMGtEDpv9Eycwqw9ehNzS8iIiIiIjIxT9Jz8dmKs0jJUJ8t/FRdV3w2oSPsbY0z2axlo1pYNL0rJvRvAQc7ucZjlAKw9/QdTFkQiuP/3Je8jAMZR+/OfpBLdMeEXGaF3p39JGnLVPTr1w/16tUDAPz6669i9QUA4qKmt2/fFsublHbixAmMGTMGY8aMUSkpk5+fj/DwcISHhyMtLQ1A0azyzp07Y/Lkydi1axf8/PxQWFiIZcuWSfZ+iuuPR0VFiZNdS7t8+XK57WRlZaltUygU2Llzp8bj161bh6ysLDg4OGDt2rUYMmQIAgICxOS6QqEQZ9dL7fz58wgPD8fjx0VrGLi7u6Ndu3YYP348tm3bhg4dOgAAfvnlF4OcnyqGCXYtvL29sW3bNixYsACdO3eGi4sLbGxsUKtWLbRq1QoTJ07Enj17sGjRInh4eBi7u0TVxql/HyL8qnppmOefrofOLU1rAdGGdV3Ro10Dte37/rqDhGT1gZ2IiIiIyJRkZOdj9oqzSEhWr6tct5Yj5kzqZPRZ4dZyGQZ1bYRlM3vguafraT0uNSMPP/x+ER8vPYOYeNYvNnfeNR0x4qWmkrQ1oldTeNd0lKQtU2FtbY0JEyYAAB4+fIjdu3eL+/r16wegaJFTTclZpVKJ3377DefPn8eVK1dQv359cV9BQQHGjRuHsWPHYteuXWqvtbOzExP7CQnq/2/XV3EZlAcPHuDAgQNq+//8809cuXJF42sdHP5bXFlTEn7RokWIjo7W+Np79+4BKJr17+bmprJPoVDg66+/RmxsrG5vooLefvttjB07FqtWrVLbZ2VlBT8/PwDSfs6kP9Y0KYNcLsfAgQNZW53IRKRk5GLFTvUB0dXJ1mRKw5Q2+uUAnI54qFKrslAhYO3+a/hobHsj9oyIiIiISLvcvEJ8seocYhPUy6/UdLXHvMld4O5sb4SeaVbT1QEfjmmHXh19sXznZTx4rHlW6dU7yXjnhxPo/+xTGNmrKWrYs2yMuRrcvTEibiUiMjpZ7zZa+tfE4G6NJeyV6RgyZAiWLl2K5ORkrFy5EoMGDYJcLkeXLl3Qo0cPhIaGYsOGDXj06BFGjRqFevXqIS4uDmvWrBHrjb/22mtwdPzv4oOjoyM6dOiA8PBw/Pjjj8jLy0OnTp3g7u6O1NRU7Nu3D2fOnAEABAQESPZeRo8ejc2bNyMtLQ2fffYZEhIS8MILL0Aul+P48eNYtGiR1tcGBQVBJpNBqVTiyy+/xJIlS+Dn54e0tDQsXrwYGzZsQIsWLXD16lW11xZfLHj8+DG++uorvPrqq1AqlYiIiMDvv/+O69evi8cqFOrrU1RGt27dsGPHDmzcuBF2dnbo2rUrvLy8kJWVhRMnTog12aX8nEl/TLATkVkQBAHLQi4jI1u9NMwbg1vB1cnOCL0qX01XBwx6oRG2HFFdqf1MRBxuxDxBgB/vgCEiIiIi01JQqMDXa88jKjZFbZ9zDVvMndQZXh41jNCz8rVq4olF07thz6lobD4SpXFRVqVSwO5T0Tj17wNMGBCIF1rXs5j629WJtVyGWRM64svV4Xol2Vv618SsCR2rdP2AqmRvb4+xY8fixx9/RGxsLPbt2yfW6l64cCGmT5+O48eP4/Dhwzh8+LDa6wcPHoypU6eqbZ87dy7Gjx+P+Ph4fP/99xrPXbt2bUyfPl2y9+Lp6YlFixbhrbfeQkZGBhYuXIiFCxeK+2vUqIEBAwZgz549aq+tV68ehg0bhs2bN+P27dvo1asXPDw8kJ6ejsLCQvTs2ROvvfYaRo0apfbaCRMm4NChQ0hLS8P69euxfv16lf0dOnRAeno6bty4gTt37kj2fgHggw8+wJUrV3Dz5k2sXLkSK1euVDvG2dkZn332maTnJf1Y5rcIEVmcvy7FISwyXm37s63q4pkg0yoNU9or3RrB3Vn9AsCqPVdYA5KIiIiITIpCKeD73y/i35uJavsc7OSYM7ETGni7GKFnurOxlmFw98ZY9mGPMv+vkJKRh+83/YNPl51FbALLxpijGvY2mDu5C0b3DtC5JrtcZoXRLwdg7uQuFn8Hw6hRo+Dk5AQAWL58uVi/3NHREcuWLcPixYvRrVs31KxZE9bW1nB3d0e3bt2wcuVKfP3115DL1dc28PPzw759+zB9+nS0adMGbm5usLa2hoeHB4KCgjBz5kzs27dP8rUKO3XqhL1794qz7W1sbODh4YHevXtjx44dZc7knj17Nj744AP4+/vDxsYGhYWFCAwMxJdffolffvkF1taa5x83bNgQe/bswbhx4+Dn5wd7e3u4urqiY8eOmD9/PtauXYv27YvuTD927JikSXYPDw+EhIRg9uzZ6NSpEzw8PGBtbQ03Nzc0a9YMU6dOxZ9//omgoCDJzkn6sxKY3TErT548QefOnVW2hYWFsQ48WbTUjDy8+e0xZGTnq2x3cbTF0g+7m+zs9ZIOnYvFL9suqW2fObYdnm2lvVYkEREREVFVEQQBS7ZH4NA59ZrC1nIZ5kzshFaNPY3Qs8q5GPUYK3dexsNE7esgyWVWGPC8P4a/2KTcpKsgCEhMzcG9hAzk5StgZytHA29neLo5WPxM+Ly8PJUk4lNPPSUu+GhsCclZOBgWg9AL95Gaqb4or5uTHXq090Hvzn4WV3OdqKL0/V1mXlIzloghIpO3fMdlteQ6ALwxOMgskusA0LNDA+w9Ha1Ww3Ltvmvo2MIbNtbqMwOIiIiIiKrSuv3XNCbXZVbAh2PamWVyHQDaNPXC4hndsOtkNLYcuYn8AvWyMQqlgJ0nbuPkxQd4fUAgnn26rlqynAlc0+Zd0xHj+7XAuL7NkZSai9iEdOQVKGBnI4evtwtqudlb/AUQIjIOloghIpP2V8RDnLkcp7b9maC6ZjXzWy6zwoT+gWrbHz3Jxv4zd43QIyIiIiKi/4Qcu4WQ47c17pv2amt0blmninskLRtrOYb2aIJlH3Yv8708Sc/Ftxv/xmcrzuL+o6LJMYUKJbYcicImC/k6AAAgAElEQVSUb0IRcvy2xuQ6AKRm5iHk+G1M+SYUW49EoVChNMh7obJZWVnB090B7ZrVxjNBddGuWW14ulv+3QVEZDxMsBORyUrLzMOykMtq251r2GLKK+ZXZ6xNgBdaN1Gf9bPlyE2kZ6nP0CciIiIiqgqHzsVg7f5rGvf9b0AgenZoUMU9Mhwvjxr4ZHwHfP56J9QpY5Z5xK0kvP39cfy6KxKfLjuDTQdvQKHUrcKuQilg48EbmL3iLLJzC6TqOhERmSgm2InIZC3fcVlj4vmNV4LgpmHRUHMwYUAgSq+9k5VTgK1Ho4zTISIiIiKq1s5ExGHp9giN+4b1bIKBL/hXcY+qRrtmtfHLB90wuncAbK01p0YKFQL2nL6Da3ef6HWOyOhkfLk6nDPZiYgsHBPsRGSSzkTE4a8I9dIwnVvWwbNP1zVCj6ThV8cFPdqrzwA6cOYu4pIyjdAjIiIiIqqu/o16jO82/Q1NE7P7dPHDqN4BVd+pKmRrI8ewF5tiyYfd0bGFt0HOERmdjJDjtwzSNhERmQYm2InI5KRl5mHZDvVZNM41bPDG4CCzr503+uVmsLNVXdS0UCFgnZbbcomIiIiIpHYj5gm+WnsehQr17Przreth8iDz/7tbV941HTFrQkfM/l9HeNesIXn7mw9FISE5S/J2SZ0gCCgoVCArpwCZ2fnIyilAQaECgqBbeR8iIn0wwU5EJmflzkikZaqXhpk8KAjuzvZG6JG0PFzsMbhrI7XtZy/H49rdZCP0iIiIiIiqk9j4dHyx6hzy8hVq+9o1q433RrSBrHRdw2qgfXNvLPmgO0a+1FStrGNlKJQCDobFSNcgqSkoVCApNQd349IRE5+BuKQsxCdnIy4pCzHxGbgbl46k1BwUFKrHPBFRZTHBTkQm5ezlOJy69FBte6dAbzzfup4RemQYg7o2goeLeh351XuucnYFERERERlMQnIWZq88i8wc9cU3mzf0wMyx7WAtr76pAlsbOYa/1BRONWwlbTf0wn3+nW8AgiDgSXouYuMzkJKRp3UhWoVSQEpGHmLjM/AkPZc/CyKSVPUdNYnI5KRn5WNZyGW17U4ONnhzcCuLukXV3s4ao3s3U9sedS8Ff11Srz1PRERERFRZT9Jz8dmKs3iSnqe2r2FdF3z2v06wt7U2Qs9MS2JqDtKz1O+orYzUzDwkpeZK2mZ1p1Aq8TAxE8lpudA1XS4ASE7LxcPETCiUXHyWiKTBBDsRmYyVOyORmqn+x/7kQS3h7mL+pWFK696+AfzquKhtX3vgGvILeOsiEREREUknMzsfn68MQ0Jyttq+OrUc8cWkznBysDFCz0zPvYQMg7Qbm5BukHaNyVgzwQVBQHxSFnLy9Pt/U06eAvFJWZzJTtUWY19aTLATkUkIi4zHyX8fqG3v2MIbL7Spb4QeGZ5cZoUJ/VuobX/8JBv7/rprhB4RERERkSXKzSvEF6vOISZePcFb09Ue8yZ3sYi1jqSiqTa9FP4IvYk/w2Lw4HGG2Sa3ZDLVNJJCYZyJQSkZeXon14vl5CmQkqE+wYuoOij9u1v6d5sqhvd+EVUxQRCQmJqDewkZyMtXwM5WjgbezvB0c7CoEigVkZGdj6UhEWrbHR1s8OYQyyoNU1rrpl5oG+CFf248Vtn+x9Eo9GjvA1cn9TrtRERERES6KihU4uu153EjNkVtn3MNG8yd1Bm1PWoYoWemy85WbpB2r999gut3nwAA3Jzt0NK/FgL9a6Klfy3U93Iyi//3WFtbQyaTQfn/5VUyMzPh6OhYpX0oKFTgSZo05XaepOXCuYYNbKwN8zMnMlWZmZniY5lMBmtrpogrg58eURVJSM7CwbAYhF64r7EMipuTHXq090Hvzn7wrlm1f6AY28pdkUjVMHNg0sCW8LDA0jClvda/Bf6NeoyS6/Fk5RZi69GbmDSwpfE6RkRERERmTaEU8MPv/+Dfm4lq+xzs5JgzsTMaeKuXLKzuGng7G/wcqRl5OH3pIU5fegig6P+DLZ6qiZb+NRHoXws+tZ0hk1Vdwl3XiWBWVlZwdnZGWloaACAtLQ3u7u6wtZV2UdiypGXm61xzvTzC/7dXy81BohaJTF9+fr74OwwAzs7OZnGBz5QxwU5kYIUKJbYfu4Uth6O0rmgOFC16E3L8NnadjMaIl5picPfGsJZb/i064VficeIf9dIw7ZvXRre2llkapjRfbxe82NEXh87Fqmw/cOYu+j7TEPU8nYzUMyIiIiIyV4IgYFlIBP6KiFPbZy2X4dPxHdGkgbsRemb6PN0c4OZkp3FilKGkZubhzOU4nLlc9PNycbT9/4R70Sx3X28XgyTc9ZkIVjLBrlAoEBMTA1dXVzg5OUEulxs0UScIAlLSsqCUsMROSlohnOytmGAkiyYIAhQKBTIzM5GWlqZSIsbZ2fAXFS0dE+xEBpSdW4AvV4cjMjpZ59colAI2HryBiFuJmDWhI2rYW+5CQxnZ+ViyXXNpmKkWXhqmtFG9AnDq3wcqdQQVSgHr9l/DJ+M7GLFnRERERGSO1h+4rjaBAwBkVsCHY9qiVRNPI/TKPFhZWaFHex+EHL9ttD6kZ+UjLDIeYZHxAIrK+fyXcK8FvzqVS7hXZiKYk5MTXFxckJ5eVNNfoVDgyZMnePLkid790ZVSKSC/UCl5u3eyZNXq/59ExVxcXODkxEl9lcUEO5GBFCqUFU6ulxQZnYwvV4dj7uQuFjuTfdXuKxoXlZkYHIiartXrFj13F3sM7tYYGw/eUNkeFhmPK9FJCPSvZaSeEREREZG52XH8FrYfu6Vx37RXn0bnlnWruEfmp3dnP+w6GV1m8llXcpkV3h3RGg8fZ+HKnSTciElBoaJiSeKM7AKcu5KAc1cSAABODkUJ98D/LynTsK4r5Dom3KWYCFa3blEMFSfZpaQUBCgUAhRKJRQKAYUKJRTKom2CZMVhVLk62cHOhnXYqXpxcXFB3bp1eXFJAkywExlIyLFbeifXi0VGJyPk+C0M69lUol6ZjvPXEnDs7/tq29s1q43u7XyM0CPjC37BH3+GxSC51II9q/dexXdvP1+lNRiJiIiIyDwdOheLNfuuadz3vwEt0LODbxX3yDx513TEiJeaqk2A0ceIXv/H3n3HR1Vm/wP/TEkmZdIzaaT3ThIIkIBCCJ2gKCpiAUQFd+1bdX+s67J+XVdZXVfXLiKigoDSCV1qgAAB0nvvvU6SKff3R0xkmJJ2p+a8Xy9fyz735s5DSGbuPc855wnBnLhfn3H6JDIUlLciq7gJmcXNyCtvgWSUWdldYgkuZ9fhcvZAwN3ago9wfydE+jsjKtAJ/h524KlI1GIzEczDwwO2trbo7OxEZ2fn0ManI8EwDKQyBv1SGSQSucL/ymTaCaJrYmVhRgF2HZFIB/6dGTnA4QLmfB7M+KaZVGiIuFwubGxsYGNjA6HQODZXNgYUYCdEC+qau/H9sXxWrvX90XzMjvU0qY1Pu3r68b9dKlrDWPDx3IMTqzXM7SzM+Xh8cRj+syNDYbywsg1nb1RjTtzE6ElPCCGEEELG5sKtGny0+4bKYw/NC8by2YE6npFxWzE3CDcLG8eVOBUV4IQVSUEKYwIzHqICnREV6IxVACRSGQoq2pBZ3ISs4ibklrWiXyJTfUE1unulSM+pR3pOPQDAUsAfyHD3d0JUoDMCJg0E3NlOBBsM1DEMA6lUqhRk7+zuR11LN+qbewb+t6UH9c09aGgVQzbKLH5temGlN/z9qWpYWxpbe3AmowoXb9Wis7tf6biNtTkSo90xO9YTIgcrPcxwYuByueDz+RM25qJNFGAnRAtS08pYKSUEBkrxUtPKsDYlgpXrGYIv9mehpaNXafypCdga5k5zpnhh/9kSlNS0K4xvO5yDxCh3mFNWBSGEEEIIUSEjvwGbt1+DqseQxQm+eGxRqO4nZeT4PC42rps+5ozvqAAnbFw3fdiWn2Z8HiL8nRDh7wTMD4FEKkdhZesvAfdm5Ja1oK9/dAF3cZ8UV3PrcTV3MODOg/8ke+SUji+4Puj2RLB+iQy1zd2obuhCdWMXqhq6UNM48OfOHgkrr6dtPh6OEAgE+p6GyRlpr//WbjEqTpRg16nSoV7/ptoql5gmCrATwjKGYXAyXbn1yXicTK/EmqXhJrHKeDW3XuX3Jy7UBcnx3nqYkWHhcTlYtywCGz+9qDDe2CrG/nMleGBukJqvJIQQYswYhkFjmxgVdZ3o65dBYM6Dt5sNRPaWJvH5TwjRrrzyFry59YrKvt53x0zChvuj6b1kjKwszLBpQyL2nCrE98MECQfxuBysWhiCFUljCxKa8bkI93NCuJ8TVs4baKlRXNU2FHDPKW1G76gD7jJkl7ATXAcGEsE2fjLwzNLY2qNyYUcbBGZcSGUMawltAGAvFMDZ3oK165EBbPT6t7Iw0+IMCWEPBdgJYVljmxhtXcobd45HW1cfmtp6IXIw7uzuLrEEH+5SLlm1suDjuQdi6Kb/F5ODRZga5jqUbTJo18kCzJ/mDTshZVYQQkaHgreGq665G6lpZTiZXqny/sFeKEByvBcWJfiaVLs4Qgh7yms78PfPL6kMuE4JdcFLq+JGvPklUY3P42Ll/BDMjvPUy3u2GZ+LUF9HhPo64sHkgazggYB7M7KKm5BT2gxx3+gC7myob+nRynW5HMDF0QqTREJMchHCUySEh0gITxchHG0t8PWhHOw5XcTa68WGiOh+iGVs9vqnTHZiDCjATgjLKuo6tXLd8roOow+wb9mfpbSBJwA8eU+k0f/d2PZESjiu5zdAfltmRk+vFN8fy8cz90frcWaEEGNCwVvDNdKS6bauPuw5XYS9Z4qpZJoQoqSuuRuvfXYRXWLlNhxhvo54ZU08bR7IIjcna6xNicCapeFoautFeV0H+iQyCMx48HGzhbO9hU4CtXweFyE+jgjxccQDc4Mgk8lRXN0+tGlqTmkzenqlWp/HeNlYmcPTRQgPkTUm/RJAnyQSwt3ZGmZ89a0xFyX4Yu+ZYtay2M9mVCHE2wFLZvpRoJ0lbPf6J8TQUYCdEJaNtjfeSFU3dGJKqIvRfuBfy6vH8SsVSuOxwSLMn0atYe7k7WaLhdN9cCStTGH8SFoZUmb5wdPFRh/TIoQYCQreGjYqmSaEsKG1oxevfZqGlg7lBVQ/D1u89tQMWJjTI782cDgciBwsDSZJiMfjItjbAcHeDrg/KQgyOYPS6vahljLZJU3o1lPAnc/jwt3Zeih4PklkjUkiG0xyEcLW2nxM13RzssaqBSHYnprHyhxlcuCTnzKRV9GKZx+YTL8341TX3I3vj+Wzcq3be/0TYsjoXYMQlnG1FJf4Yn829p4tQVyIC+JCXDA5WAShpXE8XHeLJfjwB+XWMJYCPp57iFrDqLNqYQh+vl6pUO4plzPYejAHG9dN1+PMCCGGjIK3ho1KpgkhIzFca6+unn689lkaapu7lb7W3dkaf1+fYDTPCoR9PC4HgV72CPSyx31zAiGTM9j7cxG2HsrR2ms62Vn8EkAfaOsymJEucrDSSouiFXODcLOwcdxZ0rf7+VoVymo68OraeHg4C1m77kSTmlbGWnWBTM4gNa0Ma1MiWLkeIdpCAXZCWNLYKsaRtFIcuVimtddoahPj2OVyHLtcDi6XgxBvB8SGuCAuRIRALweD7a245UA2mlS2homAi4OVHmZkHBxsLPDA3GB8cyRXYfxydh0yi5oQFeisp5kRQgwVBW8NH5VME0I0GUlrr9lxk5BV3Iyy2g6l4462FvjHhkQ42NCGjeRXPC4HPu62Wrn208sjMX+aDywFug0v8XlcbFw3fcz3PZYCnsq+9WW1Hfjde2fw8qo4TI90Z2OqEwrDMDiZXsnqNU+mV2LN0nBKzCMGjQLshIwDwzDIKmnGwfMluJRVp9AvW9vkcga5ZS3ILWvBd0fzYGNlhpjggWB7bIgLnOwMo1zxen4Djl0uVxqPCRJhwXQfPczIuNxztz+OXCxVWqD48kAW3n1xNrgGuqhCCNEPCt4aNiqZJoSoM5rWXvvOlqg8ZmNlhk0bEuDqSAksRJm3m3ZaTCZEeug8uD7IysIMmzYkYs+pQnw/zO/OIB6Xg1ULQ3DPXf746kCOUktOAOjuleKNr67gweQgPLoozGAT2QxRY5tY5eLgeLR19aGprddgWjIRogoF2AkZg94+KX6+XoVDF0pVZo7oQ2ePBOduVOPcjWoAgK+77VB2e7ifE8zN1G8SM17qSlitLfj4QGVrGB6ep9YwI2JhzsfjS8Lx3vfXFcaLq9pxJqMKSVO89DQzQoihoeCt4aOSaUKIKmNp7XUnC3MeXn86AT5u2slSJsZPZG8Je6GA1eCnvVAAZ3v9VkvweVysnB+C2XGeo97Y/bcPTEaIjwM+2n0T/VK50tfsOlmIwoo2/OGxKbATCrT9VzEJFXWdWrlueV0HBdiJQaMAOyGjUNfcjUMXSnH8SgW6xRKdvCaHAwR52aO4qn1UD+VltR0oq+3ATz8XwdyMh+hAZ8SGiBAX4oJJIiErwe3hSljNzbjolyjfqDyxLBIulFkzYnPiPLH/XDGKq9oVxrcdzkVitAcEWlw8IYQYDwreGjYqmSaEqDLe1l6DXl0dj2BvB5ZmRUwRh8NBcrwX9pwuYu2ayfFeBvMZ5OZkjbUpEVizNBxNbb0or+tAn0QGgRkPPm62cLa3UDnX5Hhv+HnY4Z9fX0Fdc4/S8RuFjXjpvTN4dQ39jo1EX79y2x1WrivRznUJYQsF2AkZBsMwyChoxKHzpUjPrQMzgtgFn8eBp6sNymrGn93+6KJQrJwXgp5eCW4VNeF6fgOu5zWgvkX5w1+dfokMV3PrcTW3HgDg4mj1y2apIkQHimA9yg2QRlrCqiq4Hh3ojEUzqDXMaHC5HKxbFoH/9/FFhfGmNjH2ny3Gg8nBepoZIcRQUPDW8FHJNCFEFTZaewFAYXUb4sJcWZgRMWWLEnyx90wxKwvyPC4HixJ8xz8plnE4HIgcLEf12eg/yQ7vvTQb//7u+tAz8+2a2sT484fnsf6+KCya4UP3RhoIzLWT/EVJZcTQUYCdEDV6eiU4dbUSB8+Xorqxa0Rf42hrgcWJvlg4wwc2VuZ47dOL47phjgpwwoqkIAAD/eVmRLpjRqQ7GIZBbVP3QLA9vwG3ippGtVLc0NKD1LQypKaVgcvlINTHAXGhLogLcUHAJHuNfb3HU8LK4QDrl0fRDckYRAeKMC3cDVdy6hTGd50sxPxpPrC3oZJFQiYyCt4aPiqZJoTciVp7EV1zc7LGqgUh2J6aN+5rrVoYYlI/b0Irc/x13XTsOlmAb4/mKSXWSWVyfLT7JvLKWvDbByZTwFcNbfX6p/ZXxNBRgJ2QO1Q1dOLQhVKcTK+EuE86oq8J93NEyix/JES5g8/jDo2PZ1fzqAAnbFw3XeF6gzgcDjxEQniIhEiZ5Q+JVIac0hZk/BJwLx1F5rxcziCntAU5pS3YfiQPttbmiAkWYUqoC2KDXeBg+2tPvfGWsDIM8OlPt7BpQ6LKvxfRbG1KOK7m1Stspivuk+K7Y3n47YrJepwZIUTfKHhr+LRVMn0pqxYeImu4O1nTAjYhRoZaexF9WDE3CDcLG1lLBDMlXC4HK+eHIMjLAZu/vYrOHuW2sKeuVqKspgOvro03qQUGttQ0doHL5Sg8s46XwIwHSwEtaBDDxmGYkTS8IIaipaUFCQkJCmNpaWlwdHTU04xMg1zO4GpePQ6eK0FGQeOIvsacz8XsOE8snemHAE97tedJZfIx7Wq+IilozEHolo7eoWB7Rn4jOnv6x3QdAPDzsB1oJxPqgqziZlaybB5bPND2hozex3tu4vDFMoUxLpeDD/+QBC9X7WQLEEIM34WbNXhrWzrr131lTTxmRnuwft2J6GpuPf7+xSWtXd/F0QoxQSLEBIkQHeRMm7ERYuAYhsHq14+yvuHkttcX0mIbGdZ4qpIHE8GsLEbXZtTY1Lf04K2vr6Dojn2wBllbmuH3j8QhPtxNxzMzTHXN3dhyIBtpmbVaub69UIDVS8KQHO+tseKeaB/FJVWjALuRoR9kdnWJJThxpRyHLpSq3NBEFRcHSyxJ9MP86T6wtTYf8WsNtyGoql3N2SCTMyiuahsKuOeVt7K6mjwWPC4Hn7ySTCv+Y9De1Yen3zyhVF0RH+6K156coadZEUL0TVvB2789NQNTqacvK2oau7DhrZM6ez3/SXaICRJhcrAIEf5OVMpOiIFpaO3Bk28cZ/26WzYuoMojMiL6SAQzNv0SGT7bm4mjl8rVnvPw/BA8vCAEvAka9BX3SbHrZAH2nimGRKq8BxvbAr3sseG+KIT6UAxMXyguqRoF2I0M/SCzo7y2AwcvlOL0tcoRl2xHBzojZZY/pkW4jevDk2GYUe1qzrZusQQ3Cxt/yW5vQEOrWOuvqcqKpEAqYR2jXScLsO1wrtL4G88kYnKQSA8zIoToGwVqDNv1vAZ8+tMt1DR16+X1zfhchPk6IiZYhMlBIgR42ms1EMAwDBrbxKio60RfvwwCcx683WwgsrekzFpCfkELo8RQ6CsRzJgcv1yOj3+8pTaAHBfigt8/OmVUCXjGjmEYnLleha8O5qClo1fnrz93qhfWLg1XaGlLdIPikqpRD3Zi0Nh8QJPJ5LicXYeD50uRWdw0oq+xMOchaaoXls70Y21TjbHsas4ma0szJEZ7IDHaAwzDoKqhayi7PbO4Gf0S7fSIvdPJ9EqsWRpOD9pjcM/dATiSVobGOxZHtuzPxnsvz6aSOUImIJG9JeyFAlZbDZjxuZDKdPOZYKrqmrvxxb4sXM6uG/5kLZJI5bhV1IRbRU0AciG0NEN0kPNQhjtb/dspSEPIyGlrX4Y+Hd3LE9Ph5mSNtSkRWLM0XK+JYIZs/nQf+E2ywz+/TkdDi3Ll+/X8Brz83s94dc00BHqpbx9rKgorW/HZT5nIK2/VeJ6lgD/ife1U4XCgtNnsoFNXK5GWWYtVC0KQMssfZvyJUVVBDBdlsBuZibJSxOYDWntXH45dLsfhi2VoahtZtra7kzWWzvJDcrw3hJam3Vvudv0SGbJLmoey28u1tGneIMqMHLufr1Xi399dVxp/6eFYJMd762FGhBB9+9/um0hNK2P1mmZ8Lu6bE4gH5wbBQkB5GSPV2yfFrlOF+OnnIq2VS3O5HCxJ9EVRZRsKKtvG1f5tvP3bpTI5dp8qxI7RtBlYEIIVcydOmwFC7kQZ7IQYn86efmz+9hqu5zWoPG7G52LDfdFYOMNHxzPTjdaOXmw7nIuTVyvUBr4BwNFWgDVLIzAt3A1vbh17r/9n7p+MncfzcfZGtcZzJ4mEWL88CnGhLqN+HTJ6EyUuOVoUYDcypv6DzOYDWlFVGw6eL8HZjOoRP9zGhbpg2Sx/xIW4UBYwgKY2MTLyG3AivQI5pS2sX58eAMZOLmfw+/+eRVFlm8K4k50FPnklGRbmFAgjZCLJyG/Av7ZdRXevRCvXd7KzwNql4Zgd5znhs9g0YRgG525U46sD2Whq12659O0bhneLJcgqbsKNwkbcKGhEVUPXuK492L89JliE8GH6t9NGeYSMDbX2IsQ4yeUMdhzPx47j+WqDzPOneeOZ+6NhbiL7n0ikchw4V4Idx/M1ZqTzeVzcNycADyYHw/KXxAw2ev1nFTfh058yUVbbofFrp0e44al7I6lKTstMPS45VhRgH4ZYLMaOHTuQmpqKoqIiSKVSeHt7Y/HixXjiiSdgaanbmxdT/kFm4wHNjM9DWmYNDp4vRW7ZyALCVhZ8zIv3xpKZfpgkEo76tSeCCzdr8Na2dNav+8qaeMyM9mD9uhNFVnETXv3ogtL4Y4tCsXJ+iB5mRAjRNbmcwe5Thdiemqsxk4gtYb6OeHp5JIK8HLT/YkamtKYdn/6UiewSzfcx4X6O6JfIUFTVPubXigpwwqYNiWqzv5vaxLhZ2IgbhY24WdCI1s6xtw66vX97TLAI/pN+7d8ulcnx2qcXx3TvNmi4vwshpophGKx+/Sirrb3shQJse30hLYQSogNXc+vx72+voUusOrkhwNMOr66ZBldHKx3PjF3pOXX4Yl/WsPvIzIh0w7plkXB3Vh3cHm+XAplMjtRL5dh+JFft9xyg6ktdMOW45HhQgF2DiooKPP300ygrK1N53NfXF19++SU8PT11NidT/UFm4wHNxcESEqkMrZ39Izrfy1WIpTP9kTTFkzKnhkElrIbr/766jEtZir19Lcx5+OzVebThCyEmrksswXvfXceVHHb7e/N5HEhl6m8PORxgXrw3Hl8SBgcbep/p6O7Ht6m5SE0rg6bELCc7CzyREoG7YydB3CfVWdY3wzCoqOscym7PKm5C7zh6P9/ev726sQv7zpaM+VqDbs/GJ2Qi2XowG3tOF7F2vRVJgVibEsHa9QghmtU1d+OfX6ejpFr1ormNlRl+/+gUTAk1vmfeyvpOfLk/C9fUtMMZ5OVqg/XLIxETPLL2LAzDjKvXf0d3P7YfyUXqpTKNySXOdhZYtywSs2I8aNGRZaYalxwvCrCrUV9fj1WrVqG6eqDXk5mZGWbNmgUHBwekp6ejsrISAODl5YUff/wRtrbsbIA5HFP9Qd55PB/bU/O0/jocDjAt3A0ps/wwOUhEb7QjRCWshqu6sQvPvn1Kqdxu4QwfPPdgjJ5mRQjRttKadvxzazpqm1VnEw0XJFcnKsAJv390Cg5dKMVPPxdDKlPfYs1SwMfD80Ow7K6JubGUTM7g6KUybD+Si84e9ZlUqsqlAXZKpsdCIpWjoKIVNwoacaOgYdz929nA43LwySvJVNJNJpy65m4889bJEb0HDId+jwjRjz6JDB/vuYmT6ZUqj3M4wKoFoVg5L9go2tB2iSXYcSwfB8+XaHxvsrY0w6MLQ7Ek0Rc8PVShFVe14bO9mcO2snjPd1EAACAASURBVI0McML65VHw87DT0cxMn6nGJceLAuxqPP7447hy5QoAIDAwEB9//DG8vQc2DpTJZNi4cSN+/PFHAMB9992Ht956SyfzMsUfZDZvLNURWpphwXQfLJnpZ/QlWvpAJayG7dOfbuHg+VKFMS4H+O8fkuDjppvFP0KI7py6Won/7b6JfonqLOTJQc54eVUcTlypGFfwtrapG1/uz8LlbM0Z8h7O1njq3kjEh7uN/i9jpLJLmvHpT7dQWqO5F+i08IFeoOrKpQF2N3YfC7b7t48VZd7qHsMwaGwTo6KuE339MgjMefB2s4HI3pLuz3SIrUQjqgQhRH8YhsHRS+X49KdMtckJU8Nc8ftH4iC0Mtfx7EZGJmdw4ko5vjmSi/Yu9V0BuBxgYYIvHl0YOupN0dnGMAzOZlRjy4FstHSo3/uGywEWJ/rh0UWhsDHQ778xMcW4JBsowK7C8ePH8dxzzwEAbGxscODAAbi7uyuc09/fj2XLlqGsrAwcDgcHDx5EYGCg1udmij/IbJdG3s7X3RYps/wxO24Sbfo4TlTCarjau/qw4Z8n0N2ruOHMlFAXvP50gpqvIoQYG4lUji/2ZeLwxTK15zyYHIRHF4UN9chmI3ibkd+Az/dlobK+U+P8poS64Ml7IuHlajPiv5OxaW4XY8uBbJzNqNZ43iSRNZ66N2pUbdDGWzLNFjb7t48GLbzrjr4XdYgiqUyOV/53HvnlrWO+Bu1lQIhhKKhoxT+/TkdTm1jlcVdHK7y6Jh4BnvY6nplm2SXN+GxvptpWN4MMNRtc3CfFrpMFw1Zf2liZ4/HFoVgww3foXpmMninGJdlAAXYVHnroIdy8eRMA8Ic//AFPP/20yvO+/fZbbNq0CQDwxBNP4JVXXtH63EztB1kbmdEAkBjtjnvuCkC4nyM9qLGESlgN24+nC/HVwRyl8U3rExAbMrJ+eIQQw9XUJsZbX6cjv0J1AMbKgo+XV8VhRqS7yuPjDd5KZXIcvliK747mo1vDxlI8LgfL7vLHw/NDYG1pOvubSKQy7D1TjB9OFGjsX24p4P3SNifAJNrmsN2/fTjUOk67pDI5dp8qxI7RVLYsCMGKueNrS0SG9872q8Mu3Kkz2n0ZCCHa1d7Vh83fXsONgkaVx835XPxmxWTMm+at45kpa2wVY+vBbJy9ofn9x8XBEuuWRSIx2t2g4ys1TV34cl/2sPsT+XvYYf19UYjwd9LRzEyLqcUl2UIB9jvU19fj7rvvBgDw+XycO3dO7Q9JT08Ppk2bBolEgoCAABw+fFjr8zO1H2Tq7W1cqITVcPVLZPjNv06ioVUxW8LX3Rb/+d0cWqEnxIjdLGzEO9uvqi3X9XGzwV/WToOHSKj1ubR39eHb1DwcvaR5Q087oTkeXxyOedO8jfr9h2EYpOfW44u9WWr73Q+aO9ULa5aGw9GEN5ge7N9++ELpsA/jY0Gbn2tPT69EZxvrktEpqmrDy++dGfXXsbUvAyGEfTI5g++O5uGHEwVqz1mU4Iv1yyNhxufpcGYD+iQy/Hi6CLtPFaptOQgA5mY8PJgchPvmBEJgpvt5jtXV3Hp8sS8T1Y2a791mx3riiWXhcLKj2NFomFpcki3UM+MON27cGPpzVFSUxh8QKysrBAUFIScnB8XFxejq6oJQqP2HW1NSUae53Hysyus6KMCuBSvmBuFmYeOYHs4GRQU4YUVSEIuzIsDAzc+apeF4Z/s1hfGy2g6cSi/H5GAX6rFKiJFhGAZ7Thfhm8M5aoPZs2M98dyDk2Eh0M0tnZ1QgN8+MBmLE33x2d5MZKn5PGjv6seHu27gSFopnr7XODOEqhu78PneTFzLa9B4XqCnHTbcF41QX9N/qDDjcxHh74S2zj6tBNj7NDzkk7GTyuRjDq4DQGZxM97YcplakGgBwzDYsj9badycz4WlBV/lwiq18CHE8PG4HDy+OAwh3g5497trSq08ASA1rQzFVW14ZU08XBx0s08cwzC4eKsWWw5kKSVm3enu2ElYuzTCKOMqU8NcMTlIhAPnirHjeD7EfarvL85kVOFydi0emheM5bMD9LLYQUwHBdjvUFxcPPTnmJiYYc8PDQ1FTs5AW4a6ujqNfdhbWjTvbjwSra1j781niPq0VGZMD2jawedxsXHd9HFnQNHDmXbcFTMJ+84Wo6CiTWH8g103oapWiR7QCDFc3WIJ3t+ZgbTMWpXHeVwOnro3Ektn+ulloczPww5v/mYmLtyqwZYD2WhU85BWXNWOV/533qge0np6Jdh5vAD7zxVDKlOfpm9rbY7VS4w/S38sBObaeQDt6VXffoiM3Z5TheNKjgAGgux7ThdSBSLL0nPqkVncpDS+NiUCKbP8DGJfBkLI2E2LcMO7L8/GP7emo6xWeWP0wso2vPTuGfzp8SmICdZuW8/SmnZ8vjdL5XvO7fwn2WH9cuNMjridGZ+L+5OCMGeKF7YezMbpa1Uqz+vtl2Hb4Vwcv1yBp5ZHYlq4m45nSkwFBdjvUFNTM/RnPz+/Yc93cvr1TWe4APudJRREew9oxlS+ZGysLMywaUMi9pwqxPej6eFJJaxax+FwsG5ZJF7533mFcXWNwNq6+rDndBH2nimmHquEGJDy2g68ufUKappUl7U62lrgldXxCPPTb8Y0h8PBrMmTEB/uNmyZ8dmMalzOrsMDcw23zFguZ/Dz9SpsPZitcWNPLpeDpTP98MiCEAitzHU4Q8Ph7aadjWw/2HkDl7PqsGyWP6KDnCmIyIK65m58fyyflWt9fzQfs2M9aVGeJVKZHFsOKGevezhbY3GiLzgcDkQOlkaxMEkIUc/DWYh3XrgLH+2+qTLI29nTj799loZHF4XhgblB4KpYtGcYBo1t4jFVJE+09n53crS1wO8emYLFCX74dO8tFFep3si1trkb//jyMqaGueKpeyMxSQetF4lpoQD7Hdrafs389PDwGPZ8O7tfd0+uq9O8kQJRpq0HNB83W61clwzg87hYOT8Es+M8kZpWhpPplSo3qqUMad3z87CFrbU5OrpV92pWRSZnsD01DzcLG6nHKiF6duZ6FT7YdUNthVdUgDP++PgUONgYTp9vgRkPqxaEYF68t8aNsvr6Zfg2NQ/HL5cb3EZZRZVt+PSnW8gr11wpGB3ojPXLo+DjPrHvM0T2lrAXCljfpJ4BcDm7Dpez6+DlaoOUWX5ImuIFSx21QDJFqWllrGxQDwzcL6SmlWFtSgQr15vojl4qR3Vjl9L42pQISnggxMRYmA9sRh/q64jP92YqVcjJGeCbI7nIL2/Fy4/EQfjLRvF1zd1jft6WyuQ4crEM3x7NG9EG9Svnhwy9rikK83PEv1+cjRNXyrHtcK7a5+WrufW4UdCAe+8OwEPzgkf0bDyeBRBiOuhu9Q49PT1Df749eK6OldWvvbKkUuW+WkQzbTyg2QsFcLY3nMCDKXNzssbalAisWRpOJawGYLDH6miC67ejHquE6I9EKsdXB7Nx4FyJ2nPunxOI1UvCwDPQ30+RgyX++PhULJnph8/2ZqKkWnWGUEOrGG9tS0dUgDOeXh4JP4/h77e0pb2rD98cycWxy+Vqq32Agb/bk/dEIjHKcBYF9InD4SA53gt7Thdp7TUq6zvx8Z5b2HYoB8nTvLF0ph88nCmbbDQYhsHJ9EpWr3kyvRJrlobT78E4dYsl+O5ontJ4hL8TZkRSewJCTBGHw8GSRD/4T7LDv75OR1N7r9I5V3Lq8Lv3BlrGXM1rwI5hKsbVVSTfKGjAZ3uzUFmvec+7uFAXPHVPJLxctZP4aGh4XA4WzvDFzGgPfHcsH4culEKu4vsrlQ3sg3T6WiXWpkRgTpynys+98SyAENNDAfY7SCS/ruxZWAwfpOXzf/0W0o3m6GnjAS053ov+LXSMSlgNA/VYJcQ4NbeL8a9tV5FbpnqvFksBHy8+HIuZ0cNX1hmCCH8nvPvS8BlCmcVNeOndn7EwwRePLQqDrbXu2q3IZHIculiK71LzVG48Nsicz8WKuUG4PykQFuZ023y7RQm+2HummLXsaHW6e6XYf7YE+8+WYEqoC1Jm+SMuxEVlCT1R1NgmZr3KoK2rD01tvXTPN067TxWqfG988p4Ieo4hxMSF+jjivZfn4J3tV3GrSLkfem1zN373/lmNC/93GqxIvpJTB1src1wdZoN2D2drPHVvJKaGuU7I9xyhlTnWL4/Cguk++Hxvpsp/BwBo6ejDu99dx5GLZVh/XxQCPe0BDCS27T5VOOYFEGKa6EnhDqN9c+Hxfu0hyuVq/kVJS0sb05xu19raiiVLloz7OoaEzQc0HpeDRQm+458UIUaGeqwSYpwyi5vw9jdX0aam57eXqw3+sjYeni7GlVk0lCE0eRJ2HMvHwfMlKj/n5Qxw5GIZzmVU45GFoViS6DuiDP3xlOLeLGzEZ3szUVGnOasrMdod65ZFwtXRSuN5E5WbkzVWLQjB9lTlLNzRigkSoaqhU2U23+2u5TXgWl4DPJytsXSmH5LjvWFtwuXs4zXcz/hYldd1UIB9HBpae7DvbLHS+Jw4TwR5OehhRoQQXbO3EWDT+gRsT83D7lOFSsdHE1y/XUFFm8bjlgI+Hp4fgmV3+cOMT4FeX3dbvPFMIi7eqsWXB7LQ2CpWeV5uWQt+958zWDDdBw/MDcJ/d2aMKrGNWrJODBRgv4NAIBj6c1eXck+8O91+znABdkdH/W5GZqjYfEBbtTCEgoJkQqIeq4QYF4ZhsPdMMbYeylFZmgoAd8VMwvMPxRh1/2mhpRmeujcSC2f44It9WbierzqjqksswWd7M5F6qQxP3xuJmGAXleeNpxS3oaUHWw5k48KtGqWvu52Xqw3WL1c/B/KrFXODcLOwcVzVU1EBTvjb0zPAAXApuw4Hz5cga5jr1TR14/N9WfjmSC6SpnohZaYfvGn/HQVyOYPy2g6tXLtPzWbGZGS+OZwLiVSuMGbG5+LxxWF6mhEhRB94PC7WLA1HsLcD/rPjOno0VNSxYV68N1YvCYODLbXTvR2Hw8HMyR6YEuaCPaeKsOd0odJ7NDCw6HH0UjmOX6lQe+8+HGrJatqM94lNS27vu97UpLpM5Ha3b4pqb2+vlTlNBGw9oK1ICmJxVoQYB+qxSohx6emV4L87b6gN9PK4HKxbFoFld/mbzO+gl6sNXn96BtJz6/HFvizUNnWrPK+irhN//TQNMyLd8OQ9kUNB8vGU4srkDH48VYjdpwrRr+KBaZC1BX8gi36mHz30jBCfx8XGddPxxpbLY7qHiwpwwsZ104e+3zOjPTAz2gOlNe04dKEUp69VoV9DMLe3X4YjF8tw5GIZJgc5I2WWP+LD3cCboO1j2jr7kFHQgOv5DbiR38h6e5hBAjPe8CcRlQorW/Hz9Sql8XvvDoALVcsQMiElRLnD2202Nn1xCTVq7o/GI9THAU8vj0KwN1XIaGJhzseji0KRHO+FLQeykZZZq/K8sQbXB1FLVtNFAfY7eHj82t+0qKgICxYs0Hh+XV3d0J9FIpHW5mXq2H5AI2QioR6rhBiPyvpOvLn1CqoaVFfJOdgI8OfV8Yjwd9LxzLSPw+FgWrgbYoNFOHCuBDuOF0Dcpzpb61JWHa7mNuC+OQFImeWPzduvjqkU92xGNXr6JGhqU996hMMZzOoKh72NQO15RDUrCzNs2pCIPacK8f0wCyCDeFwOVi0MwYok1b1I/Tzs8NyDMVizNBzHL1fg0MVSNLT0aLzmzcIm3CxsgoujFZYm+mL+dB/YWOmur78+SGVy5Ja1ICN/IKheXKV6Y2G2+VC1wJgwDIMv92crjdtam+OBuZQkRMhENkkkRHy4K/adVb/Z/WhZmPPw7AOTMVvNBp1ENTcna/xl7TRk5Dfg832ZqKwfvrPFaFFLVtNEAfY7RET82hIhIyNj2POvXbs29Gd/f3+tzGmi0MYDGiETAfVYJYZoPD2yTdW5G9X4784M9ParzsiN8HfCnx6fCkcTL9014/Nwf1IQkqZ4YdvhXJxIr1B5nlQmx66Thfjp5yJIZWPLFqqo1/z+GOLtgPX3UVbXePF5XKycH4LZcZ5jbuGjio2VOe5PCsS9swNwLbceB86X4EZBo8avaWjpwVcHc/Btah5mx3li2V3+8POw0/g1xqSuuRvX8xtwPa8Bt4qa1C5SaYu9UABne9N+j9KWy9l1yC5RXih8ZGEo7SVAyATHMAzOXK9m9ZoCcx4F18chNsQF//19Eg5dKMV3R/NYbeFDLVlNEwXY7zBjxgxwuVzI5XJcuXIFXV1dEAqFKs+tr69HRcXAQ6Gvr6/a88jIaesBjRBT1qcmWDfu61KPVTIG4+mRbaqkMjm2HsxRuandoHvvDsDalPAJtVjsYGuBFx+OxeJEX3y2NxP55a0qzxtrcF0TexsB1i4NR9IUL3AnaDsRbXBzssbalAisWRqOprZelNd1oE8ig8CMBx83WzjbW4zpQZ/H5WBahBumRbihsr4Thy6U4tTVCoj71H9O9UvlOH6lAsevVCDC3wkps/wwI9J9zL9j+lo0FPdJkVnchIy8gSx1bbQPGI3keC8K1ozBwOeAcvb6JJEQC2f46GFGhBBDoo2K5PaufqpIHic+j4t77w7AXTEeePr/TmhsNTha1JLV9FCA/Q52dnaYOnUqrly5gt7eXuzcuRNPPvmkynP37ds39OfExERdTXFC0NYDGiGmSGCunV6o1GOVjMZ4emSbclC5taMX//rmqsqsRWCgfPeFlbG4K2aSjmdmOIK9HfD2c3fhTEYVth7MQUuH+nYu48XjcnDP3QF4eH4wrCwoY1RbOBwORA6WWnmo93K1wTP3R2P1kjCcTK/EoQslqG7UHHTOLmlGdkkznOwssDjBFwtn+I64HZCuFw0ZhkFZbQeu/xJQzyltHvMikzmfiyAvB+SUNYNhYZ2Kx+VgUYLv+C80AaWmlan8OV23LMKkPwMJISNDFcmGTSpjWA2uA9SS1RRRgF2FRx55BFeuXAEAfPTRR1iwYAG8vLwUzmloaMCXX3459P/vuecenc5xotDmAxohpsLbzUYr16Ueq2Skenolo95DY7BH9s3CRmxcN90kg53ZJc3417Z0tHaqzkjydBHi1TXx8KbfNXC5HCRN8cKMSHfsOlmAn34uhlTG7oOMq6MV/vbUDHi5auc9k+iWlYUZlt3lj6Uz/XCjoBEHzpfgWl69xkByc3svtqfmYcfxAtwV44GUWf5q2wPpctGwvasPNwoacT2/ARn5DWrfM0bCy9UGcSEuiAtxQUSAEwRmPOw8no/tqXljvuagVQtDJkzlEZu6xBJ8dzRfaTwqwBnx4a56mBEhxNBQRbJhowUQMhIUYFdh/vz58PPzQ2lpKbq6urB69Wps3rwZU6ZMAQDk5eXhd7/7Hdra2gAAU6ZMQWxsrD6nTAiZwET2lrAXClgtK6Qeq2SkpDL5mDeoBoDM4ma8seUyNm1INJksPoZhcOBcCbYcyFYbmJsZ7YEXVsaY5MLCeFgK+Fi9JBzzp3nj2bdPQ8JikL2vXwZPF2rnZ2q4XA7iQl0QF+qC2qZuHLpQihNXytGtoVeqVCbH6WtVOH2tCiHeDlg6yw+zJnvAjD9QuaXtRUOZTI688lZk5DfgWn4Diqvaxpxhbm1phpggEWJ/CaqrelBfMTcINwsbx/w+DQBRAU5YkUQbcY7F7pMF6OzpVxpfd08EVeQSQgBQRbKhowUQMhIUYFeBz+fj7bffxurVqyEWi1FTU4NHHnkEPj4+4PF4KCn5dWdnKysr/O1vf9PjbAkhEx2Hw0FyvBf2nC5i7ZrUY5WM1J5TheMK2gADQfY9pwuxcl4IS7PSH3GfFB/8cAPnbqjeqIrL5WDt0nAsnx1Av2Ma8HhcVoPrAJXiTgTuztZ46t5IPLYoFKevV+Hg+ZJhs87yK1qR/10rthzIxsIZPpg/zRvv78hgfdGwoaVnYHPS/AbcLGwc82ZpHA4Q7OUwFFAP9rYHb5jFST6Pi43rpo9rMXTpTH+TWQTVpfqWHuw7W6I0njTFE4Ge9nqYESHEEFFFsmGjBRAyEhRgVyM6OhpbtmzBCy+8gMbGRgBAeXm5wjk2NjZ4//33ERJi/AEBQohxW5Tgi71nijWWsY8U9VglI1XX3I3vjymXvY/F90fzMTvW06jbD1Q1dOLNremorFcd0LMXCvCn1VMRFeCs45kZHyrFJeNhIeBjcYIvFs3wQWZxEw6eL8XlrFpo+ohs6+zDzuMF+OFEwbj7lWcWN2PniXyEeDsOBNXzGlDd2DXm6znaWgy1fZkcLIKttfmor2FlYYZNGxKx51Qhvh+m7Y0qn+/LREywCNaWVHUzGtsO5yi1uzLnc/H44nA9zYgQYoioItmw0QIIGQkKsGsQFxeHI0eO4IsvvsCePXuGAu02NjZYuHAhnn32WXh4eOh5loQQMrAx8KoFIaz0WHWwFcBeOLLN38jElppWxsqiDjDQXiE1rQxrUyJYuZ6uXbhVg/d3ZEDcpzorNczXEX9ePRVOdhTcHQkqxSVs4HA4iA4UITpQhIaWHhxJK8PRS+Uq23UMYmMzUADYcaxgzF/L53ER6e+E2BAXTAl1gbebDSsVL3weFyvnh2B2nKfGjVstBXyl97Lm9l58uT8LL6yktpgjVVDRirMZytVM984OoIU+QogCqkg2bLQAQkaCAuzDsLGxwcsvv4yXX34ZdXV1kEqlcHNzA59P3zpCiGFho8cqADS19eLNrVfw1yenD/WjJeRODMPgZHolq9c8mV6JNUvD9f4wwDAMGtvEqKjrRF+/DAJzHrzdbCCyt1Sam0wmx7bDufjxZ/UPRMvu8scTKREw41N7hZGiUlzCNhdHK6xZGo6HF4TgXEYVDpwvRUl1u76nNWSSSDjQSz7EBZEBTrAw196zhpuTNdamRGDN0nA0tfWivK4DfRIZBGY8+LjZwslOgL99fgk3ChoVvu74lQrMmjwJcaEuWpubqWAYBl/uz1IatxcK8MBc6mVPCFFGFcmGixZAyEhQlHgU3Nzc9D0FQghRi40eq4MyChrx7++u44+PTQWPSx/8RFljm5jVLA5A/z2y65q7NWZ12gsFSI73wqIEX7g5WaO1sxfvfHMNmcVNKq8nMOfh+QdjMDvOU8szNz1Uiku0RWDGw7xpPkiO90ZuWQsOnS/FhVs1rFXjjJSVBR+Tb9uc1NXRSqevDwwEDEQOlirfc59/KAbPvXNaKZP9gx8y8OEf51KrmGGkZdYip7RFafyRhSG0uTUhRCU2K5JXLQwx6raLhogWQMhwKMBOCCEmZCw9Vrm/BNDld5x74WYNhJY38ewDk2l1nSgpr+3QynW/PpSN6RHu8HQVwkMk1EnGsVQmx+5ThdgxzO9MW1cf9pwuwt4zxZg3zRtXsuvQ2ql6kcHD2Rp/WTsNPu4U0B0LKsUl2sbhcBDu54RwPyesaxdjz6kiHDivvBkle68HBHjaD/VSD/FxMOhNQ10crLBuWQT+t/umwnhTey++OpiN5x6M0dPMDJ9EKsfWQzlK416uQiyY7qOHGRFCjAUbFclRAU5YkUSVMmyjBRAyHAqwE0KIiRlpj9Xbs3HLajvwz6/TlYLsRy+VQ2hpZrR9sQl7GIZBVUMXMoubkFXcjOt59Vp5nTMZ1TjzS89aDgcQOVjB00UILxcbeLoIf/nPBnZCc1YWfnp6JaOu+pDJGRy9VK72eEKUO15cGUsZnuNApbhEl5zsLBEX6qKVAHt0oDMWTPdBTLAIdka2v8nCGT64cLMGNwoVW8UcvVSOxGgPxIVQqxhVjlwsRW1Tt9L4EykR4BnwogohRP/GW5EcFeCEjeumG/QCrjGjBRCiCQXYCSHERA3XY9XZ3mIo2OTmZI0XHorBf3ZkKF1nz+ki2FiZYwX1DJ1QGIZBRX0nsoqakFnSjOziZtZbwgw/B6ChpQcNLT24ntegcExoaTYQeHe1GQq6e7oI4epoNeIAhlQmZ6Wl0iAuB1i9JBz3JwVSIJcFVIpLdElbG+sumemHmdEeWrm2tnE4HDz3UAye33wK4j7F78+Hu27gwz8kUbuTO3T19GPH8Xyl8clBzpga5qqHGRFCjM1YKpJ5XA5WLQzBiqQgCq5rES2AEE0owE4IISZOU4/V2yXHe6NbLMHn+5Q35dp6KAdCK3MsnEGlzdowmk01tUUuHwioZxY1IatkIEu9o7tfJ689Fl1iCfLKW5FX3qowzudx4O4shJfrr0F3TxchJomESoGgPacKWQuu2wnN8cfHpmJykIiV6xEqxSW6RRvrqubqaIW1KRH4eM8thfHGVjG+OpiDZx+YrKeZGaadJwrQ2SNRGONwgHXLImnhlRAyYmOpSKb7HN2gBRCiDgXYCSGEDLnn7gB09PRj5/ECpWMf7b4BoaUZZk42zkw8QzTaTTXZJJczKK/r+CWg3oys4mZ09hhuQH2kpDIGlfWdqKzvBFCrcMzJzmKo3YyttTl2nFD+OR+r/7d2GsL8nFi7HhlApbhEV2hjXfUWzfDFhZs1uFWkuKFzaloZZka7IyaYWsUAA5/pB8+XKo3PneoF/0l2epgRIcTYjaYimegOLYAQVSjATgghRMGjC0PR1SPBoQuKD4lyBtj87VVYWcxALPVdHZexbKq5akEIVswde9aDTM6grKYdmcXNyCpuQnZJM7rEkuG/UAMuZ+Dngi2xwSJ4udqgqqELlQ2daGwVs3dxAM3tvWhu78XNwqbhTx6ly9l1FGDXAirFJbpCG+uqx+Vy8PxDMXh+82n03tFK54MfbuADahUDAPj6UA6kMrnCmLkZD48vDtPTjAghpmKkFclEt2gBhNyOAuyEEEIUcDgcrF8eha4eCc5kVCkck8oY/N/WK3jjmUSE+jjqaYbGbaybam5PzcPNwkZsXDd9RIEMmZxBaXX70Kak2SVN6O6VjmfqMOdzEerriEh/J0QGOsNednKF5wAAIABJREFUKMDzm0+z1iP7tw9MVsju6O2TorqxC1UNg/91oqqhC9WNXZBI5Rqupnsn0yuxZmk43URrAZXiEl2gjXU1c3Oyxtql4fjkp0yF8YZWMbYeysFvV0zsVjF5ZS04f7NGafy+OQFwsqOAGCGEmDJaACEABdgJIYSowOVy8NKqWHT3SnA1t17hWF+/DH///BLeenYWfNyNv/Rdl8a7qWZmcTPe2HIZmzYkKgUNZTI5iqvbkVXchMziZuSUNqNnvAF1Mx7CfB0QGeCMqABnBHvbw4yv2E9Ymz2yLQR8BHjaI8DTXmFcJmfQ2NozFHSvrP81+K6vvvFtXX1oauulG2stoVJcogu0sa5mixP9cP5WDbLu+Aw7crEMsyZ7IDpwYu5BwTAMvtyvvH+NvY0A988J1MOMCCGEEKJrFGAnhBCiEp/HxZ9XT8Xrn19Cdoniw3SXWILXPruIfz13FwWxRoGNTTUzi5ux53QhViQFoaiqDVm/tHzJKW2BuG98AXWBOQ9hvo6IDHBCVIAzgrwcYMbXnP2rjx7ZPC4Hbk7WcHOyxtQwV4Vj7V19qG7sUgi6Vzd0ob6lm9V2NqqU13VQgF3LqBSXaBNtrKsZl8vBCw/F4vl/n0bfHa1i/rtzoFWMpWDiPV5evFWrtOE2ADy2KJRa5xBCCCETxMS7AyKEEDJiFuZ8/HXddPzlowsoqWlXONbS0Ye/fjoQZHe0Nf4es9pW19yN74/ls3Kt7Ufy8MOJAvRLxtcmxcKch3A/p6GAeoCn/bAB9TsZWo9sO6EAdkIBwu/oh94vkaG2qRuVDZ24eKsG524ol/KPV59ENvxJhBVUiku0hTbW1czd2RprloTjs72KrWLqW3qw7VAONtwfraeZ6YdEKsPWQ9lK495uNpgX762HGRFCCCFEH6gpJSGEEI2sLc3w9/UJ8HBWzsSra+7B3z5LQ1ePftpyGJPUtDJW2g4MGktw3VLAw5RQF6xZGo7NL9yF799Ygr+vT8CDycEI9XUcdXB90GCP7McWhYLHHVn2MI/LwWOLQ7FpQ6JOMvzMzXjwcbfFrMmTMHeqdoIeAjPe8CcRQgza4KJhVMDYNi2eCBvrLp3phwh/5e/PwQulyCxmfxNpQ3boQhnqmnuUxtctiwDPhH8GCCGEEKKIMtgJIYQMy95GgH9sSMSfPjyH5vZehWNltR34+xeX8I8NibCYgKXhI8EwDE6mV+r8da0s+Aj3c0JUgBMiA5wRMMlOaw/8xtQj29vNRivX9XGjPQkIMQW0sa5mXC4HL6yMwfObf0a/5M5WMRn44PdJE+J+oLOnHzuPK1emxQSLEBfioocZEUIIIURfTP/OhxBCCCtcHK3wjw2J+POH59F5R8Z6Xnkr3tx6BX99csaYs6BNWWObWGWwmW3WFnxE+DsPtXzxm2Q34oxythhDj2yRvSXshQJW/03shQI421OrJEJMhTEtGuqDh7MQa5aE4fN9ipt71jX3YNuRXKxfHqWnmenOzuMF6BJLFMY4nIHsdX1/zhFCCCFEtyjATgghZMS8XG3w+tMzsPGTCxD3KWatZRQ04t3vruEPj03VeVDX0FXUdWrluhbmPEwOEiEywBlRAU7w9dB9QF0dQ+6RzeFwkBzvhT2ni1i7ZnK8FwVUCDFBxrBoqC8ps/xx4VYNckpbFMYPnCtBYpQ7IgOc9TQz7atp6sKhCyVK4/PiveHnYaeHGRFCCCFEnyjNkBBCyKgEeztg47rpKjPVz9+swcd7boJh2Os1bgr6+rWz+eWLK2Oxcd10LJ8dgABPe4MJrhuDRQm+rH2/eFwOFiX4snItQohhGlw0nBrmipnRHpga5gqRg+WEDa4DA61iXlwZC3MV9wP/3XkDvf1SPcxKN7YdyoVUpnivIzDn4dFFoXqaESGEEEL0iQLshBBCRi06UIQ/PjYVquKTRy+VY9vhXN1PyoAJzLWz+eVE6HGrLW5O1li1IISVa61aGDKhWkMQQsggD5EQjy8JVxqvbe7GN0dM814gp7QZF27VKI3fPycQTnaGV7VFCCGEEO2jADshhJAxSYhyx/MPxao8tvtUIX48XajjGRku2lTTMK2YG4SoAKdxXSMqwAkrkoJYmhEhhBifZXf5I8zXUWn8wLkSZJc062FG2sMwDLbsz1Yad7QV4P45gXqYESGEEEIMAQXYCSGEjNm8ad546t5Ilce+OpiDo5fKdTwjw9PbJ8WRi6WsX5c21Rw/Po+LjeumjznIHhXghI3rpoPPo9spQsjExeNy8MLKGKVWMQwD/Hdnhkm1ijl/owb5Fa1K448uCqOqMkIIIWQCoydCQggh43Lv3QFYOS9Y5bGPdt9QWUY9ETAMg7TMGvz2nVPYfYq9zTQH0aaa7LCyMMOmDYl4bFHoiHuy87gcPLY4FJs2JMLKwkzLMySEEMPn6WKDxxaHKY3XNHXj29Q8PcyIfRKpDFsP5yiN+7rbIjneWw8zIoQQQoihoGV2Qggh4/boolB09vTj8MUyhXE5A2zefg1WT/IRG+Kin8npQU1TFz77KRPX8hq0cn3aVJNdfB4XK+eHYHacJ1LTynAyvRJtXX1K59kLBUiO98KiBF/quU4IIXe45+4AXLhVg/xyxQzvfWeLkRjlgTA/5TYyxuTAuVI0tPQojT+xLII2GSeEEEImOAqwE0IIGTcOh4MN90WjWyzFmYwqhWNSmRxvbr2CfzyTiFAf4364Hk5vvxS7TxViz6kiSGVyrb0ObaqpHW5O1libEoE1S8PR1NaL8roO9ElkEJjx4ONmC2d7C6oaIIQQNXhcDl5cGYsX3/0ZEumvn4EMA7y/8zre/30SBGba2fRb2zq6+/HDiXyl8bhQF8RNoAQCQgghhKhGLWIIIYSwgsvl4KVVsZga5qp0rLdfhr9/fgnltR16mJluXM6qxbPvnMbO4wVqg+s8Lgcie8txvQ5tqql9HA4HIgdLTA1zxcxoD0wNc4XIwZKC64QQMgwvVxs8tihUaby60bhbxew4no/uXsVe8lwOsC4lQk8zIoQQQoghoQA7IYQQ1vB5XPx59VSEqygD7xJL8NpnF1HX3K2HmWlPXXM3Nn15CW98dUVl6figuBAXfPSnufjwj0m0qSYhhBCTde/sQIR4OyiN7ztThLyyFj3MaHxqGrtw+ILyZuXzp/vAx91WDzMihBBCiKGhJ3RCCCGssjDn469PzoC/h53SsZaOPvz104to6ejVw8zY1SeR4fujefjt26eQnlOv9jxne0u8uiYerz89Ax4iIW2qSQghxKTxuBy8+HAszPiKj5pyBvjPjgz0SWR6mtnYbD2UA5mcURizMOfh0YXKmfqEEEIImZgowE4IIYR1QkszvL5+BtydlfuE1zX34G+fpaGrp18PM2NHek4dnnvnFL47lq/QZ/Z2fB4HDyYH4eM/zUVitIdCe5HBTTU/eSUZK5ICYS8UqLyGvVCAFUmB+OSVZKycF0KZ64QQQoyCl6sNHlERgK5u7ML3R42nVUx2STPSMmuVxlfMDYKDrYUeZkQIIYQQQ0SbnBJCCNEKBxsL/GNDIv784Tk0tytmrJfVdmDTl5exaX0CLATG81FU19yNL/Zl4XJ2ncbzYoJF2HBfFDxdbDSeR5tqEkIIMVX3zQ7AxVs1KKxsUxj/6eciJES5I8TANz6Xyxl8uT9LadzR1gLL7w7Qw4wIIYQQYqgoFY4QQojWuDpaYdP6BNhYKbc1yS1rwT+/TlebAW5I+iUy7Diej2ffPqUxuO5sZ4FXVsdj0/qEYYPrt6NNNQkhhJgaHo+LFx+OVaq+kjPA+zsz0G/grWLO3ahWWhwAgMcXhxlVcgAhhBBCtI8C7IQQQrTK280Wrz+dAEsBT+nY9fwGvPvdNaXepobkWl49ntt8Gt+m5qFfzWIAj8vBiqRAfPTnZMyc7EGBcUIIIQSAj5stHlkYojReWd+F74/l62FGI9MvkWHb4RylcT8PWyRN9dLDjAghhBBiyCjATgghROuCvR3w/56YrrKH+PmbNfjkx1tgGMMKsje09ODNrVfw+ueXUNvUrfa86EBnfPCHJKxNiYAlZbQRQgghCu6fE4hAT+WNz388XYiCilY9zGh4B86VoKFVrDT+5LLIEW9QTgghhJCJgwLshBBCdGJykAh/enwqVD2XpqaV4ZsjuTqfkyoSqQw/nCjAb94+pXJjs0GOthb402NT8cYzifByHXk7GEIIIWQi4fG4eOnhOPB5ijcAcgb4z44MSKSG1SqmvasPP5wsUBqfGuaKycEiPcyIEEIIIYaOAuyEEEJ0JiHKHc8/FKPy2K6ThfjxdJGOZ6Toen4Dnt98Gt8cyVXbG5bH5eC+OYH4+M9zcVfsJGoHQwghhAzDx90WDy9Q1Sqm0+Baxew4lo+eXqnCGJcDPJESrqcZEUIIIcTQUS07IYQQnZo3zQddYim+3J+ldOyrg9kQWplhwXQfnc6psVWML/dn4cKtGo3nRQY44Zn7o+HjZqujmRFCCCGmYUVSENIya1Fc1a4wvud0ERKi3BHk5aCnmf2qqqETR9LKlMYXzPCFN332E0IIIUQNymAnhBCic8tnB+ChecEqj/1v1w1cHCbQzRaJVI7dpwrxm7dPagyuO9gI8PtHp+DN38yk4DohhBAyBnweFy+ujFVuFSNn8L6BtIrZejBHaeN1SwFP5UathBBCCCGDKMBOCCFELx5bFIrFib5K43IGeGf7NdwoaNDq698saMTzm0/j60M56OtX/VDP5XJw790B+OSVZMyJ86R2MIQQQsg4+HnYYeV85WB1eV0ndh5X7nuuS5nFTbicXac0vmJuEBxsLPQwI0IIIYQYC4NpERMSotusgOzsbPD5BvPXJ4SQCYfD4eCZ+6LRLZbgbEa1wjGpTI7/++oK3ngmESE+jqy+bnO7GF/sy8L5m5qz5MP9HPHM/dHw87Bj9fUJIYSQieyBuUFIu1WLkhrFVjG7ThViRpQ7Aj3tdT4nuZzBFhWt65ztLHDv3QE6nw8hhBBCjItBRZjz87W/wU1VVRWSk5O1/jqEEEKGx+Vy8PKqOHSLJbiWp5ix3tsvw+ufX8Jbz81SaMvCMAwa28SoqOtEX78MAnMevN1sILK31JhhLpXJsf9sCb4/lodeNRnrAGBvI8ATKRFImkIZ64QQQgjb+DwuXloVi5ffO6PQjmWwVcy7L82GGV+3hdZnM6pQdEdveAB4fEkYLMwN6pGZEEIIIQaI7hYIIYToFZ/HxStr4vHap2nILWtRONYlluC1T9Pwr+dmAQBS08pwMr0SbV19StexFwqQHO+FRQm+cHOyVjh2q6gRn/yYicr6TrXz4HKApbP88cjCUAgtzcb/FyOEEEKISn4edlg5LxjfHVNMsCqr7cAPJwrw6KJQnc2lTyLD14dzlcb9J9lhTpyXzuZBCCGEEONFAXY1Nm/ejM8//3xE57q6uuLs2bNanhEhhJguC3M+XntqBv7y0XmU1nQoHGvp6MXL752BuE+qtPHY7dq6+rDndBH2ninGqgUhWDE3CO1dfdhyIFupBc2dwnwd8ZsV1A6GEEII0ZUHkoORllWr9Lm/62QBZkS6IUBHrWL2ny1GU5tYafzJeyLA5VIlGyGEEEKGR5ucqnHr1i19T4EQQiYUoaUZ/r4+Ae7O1krHusQSjcH128nkDLan5uE3/zqJZ946qTG4bic0x0sPx+KtZ2dRcJ0QQgjRITM+Fy89HAfeHUFsmZzB+zszIJHKtT6Hts4+7DpZqDQ+LdwN0YEirb8+IYQQQkwDZbCrIJfLkZU1sMmNp6cnZs+erfF8W1tbjccJIYSMjIONBf6xIRF/+uAcWjp6x3WtuuYetce4HGBxoh8eWxQKoZX5uF6HEEIIIWPjP8kODyYHY8dxxVYxpTUd2H2yAKsWardVzPfH8iDukyqMcbkcrE0J1+rrEkIIIcS0UIBdhaKiInR3dwMA5s2bh1dffVXPMyKEkInD1dEK/9iQgJf/cwb9Evaz10J8HPDM/dEI1FHpOSGEEELUe2heMC5l1aKsVrFVzM4T/7+9Ow9vqkr8P/7pyiJCW3YEUZYWAUFUkE0QC44CAoIVAUGGUTYBRxBlBJVhHPZFQBEdRQX5iggjuIGyiDgO4rjQQZYiCAxboZS2tFBom+b3R3+9Jm26JTdplvfreXiem+Tk3JPwye3Jyb3nHFL7m+u67QqzE2fTtfm744Xuv7d9QzWofa1b9gkAAPwTU8Q4EB8fb2y3aNGiHFsCAIEpPCxEOZbSTQlTWlWvCdeEh27R3HF3MrgOAICXCAsN1pMPtyk037kl16qX3/9ZORb3TBXzzqf7lVtg+rlKFUI16B7PLbAKAAD8AwPsDtjOv966detybAkABKbNu44V+tLrikb1qmr5lFj1uKMhC5YBAOBlmtSPUNzdTQvd/9vpNK3bXniOdFf993CSvt+fWOj+uNimiri2gun7AwAA/o0Bdgf27NkjSapevboaNmxYzq0BgMBitVq17T8nTK3zwsWrqlIpzNQ6AQCAeQb2iFbDOoWnZvlgS4KOnk4zbT+5uVa99fG+QvfXjKykPl0am7YfAAAQOBhgLyAjI0OHDx+WJLVt27acWwMAgScpNVOpGVdNrTM146rOp7q2aCoAAHCfsNAQh1PF5FisWvyBeVPF7PjphH47VXjAfth9N6lCWIgp+wAAAIGFAfYC9u7dq9zcvM5bZGSk5s2bp/79+6t9+/Zq1aqVYmNj9eSTT2rTpk1GOQCAef6XmO6Weo8nXiy5EAAAKDdNG0RqQLcmhe4/cjJN679yfaqYK1k5WvX5gUL3N2kQoS5t6rtcPwAACEyh5d0Ab/Pzzz8b2++//36hx0+ePKmTJ09q8+bNuummmzR37lxFR0eXqu4LFy643L6UlBSX6wAAb3Y1y+KeerPdUy8AADDPoHti9N0viTpx1v4H9zVfJqh9i7pqWLeq03Vv3HlE59MKX9E24v4WrNECAACcxgB7AbYD7JJ08803q2vXrmrQoIGysrK0f/9+bdiwQZmZmTpw4IBGjBihdevWqU6dOiXW3aFDB3c1GwD8RoVw91yezWXfAAB4v7DQEP354TaavGSnbNc7z7FY9fKanzR/QheFhJT9QuyU9Cta72DB1Dta1NHNjWu40mQAABDgGGC3kZubayxwWrFiRc2cOVO9evUqVO6JJ57QiBEjdOjQISUlJWn+/PmaP3++p5sLAH7pegcLnJmhYR3nz3gDAACeE319pPp3a6p1BQbED59M0z93HFZcbOmuILb1f18kKPOq/dVsIcFBGt67uUttBQAAYA52Gzk5OZo1a5amTZum5cuXOxxcl6SaNWvqrbfeUqVKlSRJn332mc6dO+fJpgKA36oZUUkRVSqYWmdElQqqEVHR1DoBAID7DLonRg1qVyl0//99kVDmdVX+l3hRX353rND993W4QfVrueeHfQAAEDgYYLcRHh6u7t27a+jQoSVO51KrVi3FxcVJyjvzfdu2bZ5oIgD4vaCgIMW2bWBqnbFtGygoiLlVAQDwFeFhIXpyYBsVnBo9x5KrxWt+lsWSW+q63v50v910M5JUuWKoHr4nxoSWAgCAQMcUMS5o2bKlsb13714NGjSo2PK7du1yeZ8pKSnq2bOny/UAgDe7t8MN2vD1EVkKfht2QkhwkO7tcIPrjQIAAB4V0zBKD9zVROu/Omx3/68nUvXR10f04N1NS6xjz6Fz+uHA2UL3PxQbrWomXzEHAAACEwPsLoiMjDS2z58/X2L5qKgodzYHAPxGnerXaNA9MXpv80GX6xr0hxjVqX6NCa0CAACeNvgPzfTdL4k6lZRhd//qzQfVrnltXV/MGiuWXKtWfLKv0P21Iivp/jsbmd5WAAAQmJgixgUXLlwwtq1W18+yBAD8bsDdTXVz4+ou1XFz4+oa0K3ks9sAAIB3Cg8L0Z8fbqOCM73lWHK1+IPip4r56ocTOnq68Hztw3o2V3hYiNlNBQAAAYoBdhs//fSTFi1apMmTJ5eqfHx8vLFdu3ZtdzULAAJSaEiwpo24w+lB9psbV9e0EXcoNIQ/dQAA+LJmN0Spb5fGhe4/9L9Ubdx5RFarVedSLuuHA2f1bfxp/XDgrE6cTdfKz/cXek7TBhG685brPNFsAAAQIJgixsaKFSu0ZcsWSdKYMWPUqFHRlw1evXpVW7duNW7ffvvtbm8fAASayhXDNGNUR63f/qve/zKhVHOyhwQHadAfYjSgW1MG1wEA8BOP3HeT/rM/UaeSLtnd/+5n+7Vu+2GlX84qVT1/6tNSwQVXTgUAAHABIw82YmNjje2lS5cWW/aVV17RuXPnJElVqlRRt27d3No2AAhUoSHBGtgjRsunxGpAtyaKKGJBsogqFTSgWxMtnxKrgd1jGFwHAMCPVAgL0YSBhaeKybWq1IPrd7SsoxaNXJt+DgAAoCDOYLfRq1cvLViwQElJSfr888/VunVrDR8+3K5Mbm6ulixZojfeeMO477HHHlO1atU83FoACCx1ql+j4b1b6NFezXU+9YqOJ17U1WyLKoSFqGGdqqoRUVFBBb91AwAAv9H8xuq6r8ON+vzfR516fsrFK7p8JVuVK4aZ3DIAABDIGGC3ER4err/97W8aM2aMrFarZs2apY0bN6pTp06qVq2aLly4oK+++kpHj/7eoevdu7dGjx5djq0GgMASFBSkmpGVVDOyUnk3BQAAeFCOJVfHz6Q5/fxD/0vVSyt2a8aojlzpBgAATMMAewHdunXTwoUL9fzzzysjI0P79+/X/v2FF8epWrWqxo0bp2HDhnHGJAAAAAC42frtv2rf0Qsu1bH3SLLWf/WrBnaPMalVAAAg0DHA7kDPnj11++2367333tP27dt1/PhxhYa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\n",
      "text/plain": [
       "<Figure size 1560x1200 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "from statsmodels.tsa.seasonal import seasonal_decompose\n",
    "def decompose(timeseries):\n",
    "    \n",
    "    # 返回包含三个部分 trend（趋势部分） ， seasonal（季节性部分） 和residual (残留部分)\n",
    "    decomposition = seasonal_decompose(timeseries)\n",
    "    \n",
    "    trend = decomposition.trend\n",
    "    seasonal = decomposition.seasonal\n",
    "    residual = decomposition.resid\n",
    "    plt.figure(figsize=(13,10))\n",
    "    plt.subplot(411)\n",
    "    plt.plot(df, label='Original')\n",
    "    plt.legend(loc='best',fontsize=18)\n",
    "\n",
    "    plt.subplot(412)\n",
    "    plt.plot(trend, label='Trend')\n",
    "    plt.legend(loc='best',fontsize=18)\n",
    "    plt.subplot(413)\n",
    "    plt.plot(seasonal,label='Seasonality')\n",
    "    plt.legend(loc='best',fontsize=18)\n",
    "    plt.subplot(414)\n",
    "    plt.plot(residual, label='Residuals',marker='o')\n",
    "    plt.legend(loc='best',fontsize=18)\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "    return trend , seasonal, residual\n",
    "trend , seasonal, residual = decompose(df)\n",
    "plt.rcParams['font.family'] = ['KaiTi']\n",
    "residual.dropna(inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "velvet-damages",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": true
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
